{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.simplefilter(action='ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from sklearn.datasets import fetch_20newsgroups\n", "\n", "from nltk.stem import SnowballStemmer\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import MultinomialNB\n", "\n", "from sklearn.base import BaseEstimator, ClassifierMixin\n", "from sklearn.decomposition import TruncatedSVD\n", "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "\n", "from sklearn.svm import SVC\n", "\n", "from sklearn.manifold import SpectralEmbedding\n", "\n", "from sklearn import metrics\n", "\n", "import scipy\n", "import time\n", "import keras\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class PairwiseLearner(BaseEstimator, ClassifierMixin):\n", " def __init__(self, learnertype = SGDClassifier):\n", " self.learnertype = learnertype\n", " \n", " def fit(self, X, y):\n", " self.learners = {}\n", " self.labels = list(set(y))\n", " for i in self.labels:\n", " for j in self.labels:\n", " if j<=i:\n", " continue\n", " self.learners[(i,j)] = self.__generate_new_learner(X,y,i,j)\n", " \n", " def predict(self, X):\n", " return scipy.stats.mode(self.__transform(X), axis=1)[0].flatten()\n", " \n", " def score(self, X, y):\n", " pred = self.predict(X)\n", " return metrics.f1_score(y, pred, average='macro')\n", " \n", " def __indices_for_labels(self, y, labels):\n", " return np.where([i in labels for i in y])[0]\n", " \n", " def __generate_new_learner(self, X, y, i, j):\n", " indices = self.__indices_for_labels(y, [i,j])\n", " X_t = X[indices]\n", " y_t = y[indices]\n", " le = LabelEncoder()\n", " y_t_n = le.fit_transform(y_t)\n", " learner = self.learnertype()\n", " learner.fit(X_t, y_t_n)\n", " return learner\n", " \n", " def __transform(self, X):\n", " preds = np.zeros((np.shape(X)[0], 0))\n", " for labels, learner in self.learners.items():\n", " pred = np.where(np.array(learner.predict(X)).reshape(np.shape(preds)[0],1)==1, labels[1], labels[0])\n", " preds = np.concatenate((preds, pred), axis=1)\n", " return np.int_(preds)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class MyClassifier(BaseEstimator, ClassifierMixin):\n", " def __init__(self, svd_dims = 200, pca_dims = 200, n_feature_words = 200, scale = True, final_step = None):\n", " self.svd_dims = svd_dims\n", " self.pca_dims = pca_dims\n", " self.n_feature_words = n_feature_words\n", " self.scale = scale\n", " self.final_step = final_step\n", " \n", " def fit(self, X, y):\n", " # Initiate all class relevant variables and learner.\n", " self.n = np.size(y)\n", " self.num_classes = self.__calc_num_classes(y)\n", " self.truncate_svd = TruncatedSVD(n_components=self.svd_dims, n_iter=20)\n", " self.pca = PCA(n_components=self.pca_dims)\n", " self.multinomial_nb = MultinomialNB()\n", " self.scaler_1 = StandardScaler()\n", " self.scaler_2 = StandardScaler()\n", " self.final = self.__get_final(self.pca_dims)\n", " \n", " # Do a first dimensionality reduction from the extreamly high dimensional start space with SVD.\n", " X_svd = self.truncate_svd.fit_transform(X)\n", " \n", " # Perfom classification with MultinomialNB, to get most important feature words.\n", " self.multinomial_nb.fit(X, y)\n", " self.top_feature_words = self.__calc_top_feature_words(y, self.n_feature_words)\n", " \n", " # Combine vectors belonging to feature words and SVD reduced vectors.\n", " X_combined = np.concatenate((X_svd, X[:,self.top_feature_words].toarray()), axis=1) if (self.n_feature_words>0) else X_svd\n", " \n", " # Scale if set.\n", " X_combined_s = self.scaler_1.fit_transform(X_combined) if self.scale else X_combined\n", " \n", " # Do a PCA dimensionality reduction on the combined data.\n", " X_pca = self.pca.fit_transform(X_combined_s)\n", " \n", " # Scale if set.\n", " X_pca_s = self.scaler_2.fit_transform(X_pca) if self.scale else X_pca\n", " \n", " # Fit the final step.\n", " self.__fit_final(X_pca_s, self.__final_dep_y(y))\n", " \n", " return self\n", " \n", " def score(self, X, y):\n", " X_pre = self.pre_pred(X)\n", " return self.__final_score(X_pre, self.__final_dep_y(y))\n", " \n", " def predict(self, X):\n", " X_pre = self.pre_pred(X)\n", " return self.__final_predict(X_pre)\n", " \n", " def pre_pred(self, X):\n", " # Does all the transformations, but the last step. Analogous to the fit steps.\n", " X_svd = self.truncate_svd.transform(X)\n", " X_combined = np.concatenate((X_svd, X[:,self.top_feature_words].toarray()), axis=1) if (self.n_feature_words>0) else X_svd\n", " X_combined_s = self.scaler_1.transform(X_combined) if self.scale else X_combined\n", " X_pca = self.pca.transform(X_combined_s)\n", " X_pca_s = self.scaler_2.transform(X_pca) if self.scale else X_pca \n", " return X_pca_s\n", " \n", " def __final_predict(self, X):\n", " if(self.final_step == 'svc'):\n", " return self.final.predict(X)\n", " if(self.final_step == 'nn'):\n", " return np.argmax(self.final.predict(X), axis=1)\n", "\n", " def __fit_final(self, X, y):\n", " # Fits the final step depending on which method was choosen.\n", " if(self.final_step == 'svc'):\n", " self.final.fit(X, y)\n", " return\n", " if(self.final_step == 'nn'):\n", " self.final.fit(X, y, batch_size = 400, epochs = 100, verbose=0)\n", " return\n", " \n", " def __final_score(self, X, y):\n", " # Calculates the score of the final step depending on which method was choosen.\n", " if(self.final_step == 'svc'):\n", " return self.final.score(X, y)\n", " if(self.final_step == 'nn'):\n", " loss, acc = self.final.evaluate(x=X, y=y, verbose=0)\n", " return acc\n", " \n", " def __final_dep_y(self, y):\n", " if(self.final_step == 'nn'):\n", " return keras.utils.to_categorical(y, self.num_classes)\n", " return y\n", " def __calc_num_classes(self, y):\n", " covered_y = []\n", " for i in y:\n", " if i in covered_y:\n", " continue\n", " covered_y = set().union(covered_y, [i])\n", " return np.size(list(covered_y))\n", " \n", " def __calc_top_feature_words(self, y, n):\n", " all_tops = []\n", " num_steps = self.num_classes if (self.num_classes > 2) else 1 \n", " for i in range(num_steps):\n", " tops = np.argsort(self.multinomial_nb.coef_[i])[-n:]\n", " all_tops = set().union(all_tops, tops)\n", " return list(all_tops)\n", " \n", " def __get_final(self, input_dim = None):\n", " if(self.final_step == 'svc'):\n", " return SVC()\n", " if(self.final_step == 'nn'):\n", " nn = keras.models.Sequential()\n", " dropout = self.n/(100.*input_dim + 100.*100)\n", " nn.add(keras.layers.Dense(units=100, activation='relu', input_dim=input_dim))\n", " nn.add(keras.layers.Dropout(dropout))\n", " nn.add(keras.layers.Dense(units=100, activation='relu'))\n", " nn.add(keras.layers.Dropout(dropout))\n", " nn.add(keras.layers.Dense(units=self.num_classes, activation='softmax'))\n", " nn.compile(optimizer ='adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", " return nn\n", " raise ValueError('Invalid last step!')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "class MyClassifierNN(MyClassifier):\n", " def __init__(self, svd_dims = 200, pca_dims = 200, scale=True, n_feature_words = 200):\n", " MyClassifier.__init__(self, svd_dims = svd_dims, pca_dims = pca_dims,\n", " n_feature_words = n_feature_words, scale = scale, final_step='nn')\n", "\n", "class MyClassifierSVC(MyClassifier):\n", " def __init__(self, svd_dims = 200, pca_dims = 200, scale=True, n_feature_words = 200):\n", " MyClassifier.__init__(self, svd_dims = svd_dims, pca_dims = pca_dims,\n", " n_feature_words = n_feature_words, scale = scale, final_step='svc')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "class StemmedCountVectorizer(TfidfVectorizer):\n", " def build_analyzer(self):\n", " english_stemmer = SnowballStemmer('english')\n", " analyzer = super(StemmedCountVectorizer, self).build_analyzer()\n", " return lambda doc: ([english_stemmer.stem(w) for w in analyzer(doc)])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "class TrainTestDataAndLabels:\n", " def __init__(self, train_data, test_data, train_labels, test_labels):\n", " self.train_data = train_data\n", " self.test_data = test_data\n", " self.train_labels = train_labels\n", " self.test_labels = test_labels" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "class ClassifierWithCrossValidationParams:\n", " def __init__(self, name, classifier, params, default_params = {}):\n", " self.name = name\n", " self.classifier = classifier\n", " self.params = params\n", " self.default_params = default_params\n", "\n", "def get_classifier_with_params(name):\n", " if(name == \"SGDClassifier\"):\n", " classifier = SGDClassifier\n", " params = {'max_iter':[500],\n", " 'penalty':['none', 'l2', 'l1', 'elasticnet'],\n", " 'alpha':[.001, .0001, .00001],\n", " 'n_jobs':[-1],\n", " 'learning_rate':['constant', 'optimal', 'invscaling'],\n", " 'eta0':[.1,.01,.001]}\n", " return ClassifierWithCrossValidationParams(name, classifier, params)\n", " if(name == \"LogisticRegression\"):\n", " classifier = LogisticRegression\n", " params = [{'max_iter':[500], 'penalty':['l2', 'l1', 'multinomial'],\n", " 'C':[.1, 1, 10, 100, 1000, 2000],\n", " 'multi_class':['ovr', 'multinomial'],\n", " 'n_jobs':[-1],\n", " 'solver':['saga']},\n", " {'max_iter':[500], 'penalty':['l2'],\n", " 'C':[.1, 1, 10, 100, 1000, 2000],\n", " 'multi_class':['ovr', 'multinomial'],\n", " 'n_jobs':[-1],\n", " 'solver':['lbfgs', 'sag', 'newton-cg']},\n", " {'max_iter':[500], 'penalty':['l1', 'l2'],\n", " 'C':[.1, 1, 10, 100, 1000, 2000],\n", " 'multi_class':['ovr'],\n", " 'n_jobs':[-1],\n", " 'solver':['liblinear']}\n", " ]\n", " return ClassifierWithCrossValidationParams(name, classifier, params)\n", " if(name == \"MultinomialNB\"):\n", " classifier = MultinomialNB\n", " params = None\n", " return ClassifierWithCrossValidationParams(name, classifier, params)\n", " if(name == \"MyClassifierNN\"):\n", " classifier = MyClassifierNN\n", " params = {'svd_dims':[200,100,10],\n", " 'pca_dims':[200,100,10],\n", " 'n_feature_words':[500,200,100],\n", " 'scale':[True, False]\n", " }\n", " return ClassifierWithCrossValidationParams(name, classifier, params)\n", " if(name == \"MyClassifierSVC\"):\n", " classifier = MyClassifierSVC\n", " params = {'svd_dims':[200,100,10],\n", " 'pca_dims':[200,100,10],\n", " 'n_feature_words':[500,200,100],\n", " 'scale':[True, False]\n", " }\n", " return ClassifierWithCrossValidationParams(name, classifier, params)\n", " if(name == \"MyClassifierSVCnoFeatureWords\"):\n", " classifier = MyClassifierSVC\n", " params = None\n", " default_params = {'n_feature_words':0}\n", " return ClassifierWithCrossValidationParams(name, classifier, params, default_params)\n", " if(name == \"PairwiseLearner\"):\n", " classifier = PairwiseLearner\n", " params = None\n", " return ClassifierWithCrossValidationParams(name, classifier, params)\n", " raise ValueError('Invalid name!')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def fair_score(classifier, data):\n", " pred = classifier.predict(data.test_data)\n", " return metrics.f1_score(data.test_labels, pred, average='macro')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def generate_confusion_matrix_filename(run_name, classifier_name):\n", " return \"outputdata/\" + run_name + \"_\" + classifier_name + \"_confusionmatrix.csv\"\n", "\n", "def save_confusion_matrix(matrix, classes, filename):\n", " prepared_matrix = np.concatenate((np.array(classes).reshape(np.shape(classes)[0],1),\n", " matrix), axis = 1)\n", " df = pd.DataFrame(prepared_matrix)\n", " df.to_csv(filename, index=False, header=[''] + classes)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def test_classifier(data, name, categories, cross_validate = False, print_all_cv_scores=False, consider_confusion_matrix=False, run_name = \"\"):\n", " classifier_with_params = get_classifier_with_params(name)\n", " \n", " print(\"=== Test \" + name +\" ===\")\n", " \n", " classifier = classifier_with_params.classifier(**classifier_with_params.default_params)\n", " \n", " if cross_validate and classifier_with_params.params != None:\n", " print(\"Running cross validation...\")\n", " cross_validation_params = classifier_with_params.params\n", " cross_validator = GridSearchCV(classifier, cross_validation_params, verbose=1)\n", " cross_validator.fit(data.train_data, data.train_labels)\n", " \n", " if(print_all_cv_scores):\n", " print(\"Datailed List of all CV scores:\")\n", " for mean_score, parms in zip(cross_validator.cv_results_['mean_test_score'],\n", " cross_validator.cv_results_['params']):\n", " print(\" Score: \" + str(mean_score) + \" for Parms:\")\n", " print(\" \"+str(parms))\n", " \n", " print(\"Best parms found by the cross validation :\")\n", " print(\" \"+str(cross_validator.best_params_))\n", " \n", " classifier = classifier_with_params.classifier(**cross_validator.best_params_)\n", " \n", " print(\"Training classifier ...\")\n", " print(classifier)\n", " training_time = time.time()\n", " classifier.fit(data.train_data, data.train_labels)\n", " training_time = time.time() - training_time\n", " print(\"Training took \" + str(np.around(training_time, decimals=2)) + \"s.\")\n", " print(\"Scored accuracy on Test Data: \" + str(fair_score(classifier, data)))\n", " if(consider_confusion_matrix):\n", " print(\"Confusion Matrix:\")\n", " confusion_matrix = metrics.confusion_matrix(data.test_labels, classifier.predict(data.test_data))\n", " print(confusion_matrix)\n", " filename = generate_confusion_matrix_filename(run_name, name)\n", " print(\"Saving Confusion Matrix to \" + filename)\n", " save_confusion_matrix(confusion_matrix, categories, filename)\n", " \n", " print(\"\\n\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def get_raw_data(categories, stemming=True, params = {'ngram_range':(1,1), 'stop_words':'english'}):\n", " newsgroups_train = fetch_20newsgroups(subset = 'train', remove=('headers', 'footers', 'quotes'), categories=categories)\n", " newsgroups_test = fetch_20newsgroups(subset = 'test', remove=('headers', 'footers', 'quotes'), categories=categories)\n", " \n", " vectorizer = TfidfVectorizer()\n", " if stemming:\n", " vectorizer = StemmedCountVectorizer()\n", " vectorizer.set_params(**params)\n", " \n", " train_data = vectorizer.fit_transform(newsgroups_train.data)\n", " test_data = vectorizer.transform(newsgroups_test.data)\n", " train_labels = newsgroups_train.target\n", " test_labels = newsgroups_test.target\n", " \n", " print(\"Training data shape: \" + str(np.shape(train_data)))\n", " print(\"Test data shape: \" + str(np.shape(test_data)))\n", "\n", " return TrainTestDataAndLabels(train_data=train_data, test_data=test_data,\n", " train_labels=train_labels, test_labels=test_labels)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def build_and_test_everything(categories,\n", " stemming=True,\n", " vectorizer_params = {'ngram_range':(1,1), 'stop_words':'english'},\n", " cross_validate = False,\n", " print_all_cv_scores=False,\n", " consider_confusion_matrix=False,\n", " run_name = ''):\n", " print(\"Collecting raw data ...\")\n", " data = get_raw_data(categories, stemming=stemming, params=vectorizer_params)\n", " \n", " print(\"\\n\")\n", " for classifier in [\"MultinomialNB\", \"MyClassifierNN\", \"MyClassifierSVC\", \"SGDClassifier\",\n", " \"LogisticRegression\", \"PairwiseLearner\", \"MyClassifierSVCnoFeatureWords\"]:\n", " test_classifier(data, classifier, categories,\n", " cross_validate=cross_validate,\n", " print_all_cv_scores=print_all_cv_scores,\n", " consider_confusion_matrix=consider_confusion_matrix, run_name = run_name)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def print_top_feature_words(categories, n = 10):\n", " newsgroups_train = fetch_20newsgroups(subset = 'train',\n", " remove=('headers', 'footers', 'quotes'),\n", " categories=categories)\n", " \n", " vectorizer = TfidfVectorizer(ngram_range=(1,1), stop_words='english')\n", " \n", " data_train = vectorizer.fit_transform(newsgroups_train.data)\n", " \n", " classifier = MultinomialNB(alpha=.01)\n", " classifier.fit(data_train, newsgroups_train.target)\n", " \n", " feature_names = np.asarray(vectorizer.get_feature_names())\n", " for i, category in enumerate(categories):\n", " tops = np.argsort(classifier.coef_[i])[-n:]\n", " print(\"%s: %s\" % (category, \" \".join(feature_names[tops])))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def print_mixed_up(categories, orig_cat, dest_cat, n = 1):\n", " for i in range(np.size(categories)):\n", " if(categories[i] == orig_cat):\n", " orig = i\n", " if(categories[i] == dest_cat):\n", " dest = i\n", " \n", " newsgroups_train = fetch_20newsgroups(subset = 'train', remove=('headers', 'footers', 'quotes'), categories=categories)\n", " newsgroups_test = fetch_20newsgroups(subset = 'test', remove=('headers', 'footers', 'quotes'), categories=categories)\n", " \n", " vectorizer = TfidfVectorizer(ngram_range=(1,1), stop_words='english')\n", " \n", " data_train = vectorizer.fit_transform(newsgroups_train.data)\n", " data_test = vectorizer.transform(newsgroups_test.data)\n", " \n", " classifier = MultinomialNB(alpha=.01)\n", " classifier.fit(data_train, newsgroups_train.target)\n", " \n", " pred = classifier.predict(data_test)\n", " counter = 0\n", " print(\" = Missmatch Examples for \" + categories[orig] + \" categorized as \" + categories[dest] +\". = \\n\")\n", " for i in range(len(pred)):\n", " o = newsgroups_test.target[i]\n", " d = pred[i]\n", " if(o == orig and d == dest):\n", " counter = counter + 1\n", " print(\"Missmatch Example \" + str(counter) +\":\")\n", " print(\"\\\"\\\"\\\"\")\n", " print(newsgroups_test.data[i])\n", " print(\"\\\"\\\"\\\"\")\n", " print()\n", " if counter == n:\n", " return" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def plot_2d_data(data, stage_names, labels):\n", " fig = plt.figure()\n", " \n", " colors = matplotlib.cm.ScalarMappable().to_rgba(range(max(labels)+1))\n", "\n", " plt.scatter(data[:,0],data[:,1], c = colors[labels])\n", "\n", " legend_stages = []\n", " for i in range(np.size(stage_names)):\n", " legend_stages = legend_stages + [mpatches.Patch(color=colors[i], label=stage_names[i])]\n", "\n", " plt.legend(handles=legend_stages)\n", " \n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def make_2d_reduction_pca(categories):\n", " print(\"Collecting Raw Data...\")\n", " raw_data = get_raw_data(categories)\n", " reducer = MyClassifierSVC(pca_dims=2, scale=False, svd_dims=200, n_feature_words=0)\n", " \n", " print(\"Fitting the Reducer...\")\n", " reducer.fit(raw_data.train_data, raw_data.train_labels)\n", " \n", " print(\"Reducing the Data...\")\n", " reduced_train_data = reducer.pre_pred(raw_data.train_data)\n", " reduced_test_data = reducer.pre_pred(raw_data.test_data)\n", " print(\"Reduced Training Data\")\n", " plot_2d_data(reduced_train_data, categories, raw_data.train_labels)\n", " print(\"Reduced Test Data\")\n", " plot_2d_data(reduced_test_data, categories, raw_data.test_labels)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def make_2d_reduction_spectral(categories):\n", " print(\"Collecting Raw Data...\")\n", " raw_data = get_raw_data(categories)\n", " reducer_svd = TruncatedSVD(n_components=200, n_iter=20)\n", " reducer_spectral = SpectralEmbedding()\n", " \n", " print(\"Fitting the Reducer...\")\n", " data_train_trafo = reducer_svd.fit_transform(raw_data.train_data)\n", " reduced_train_data = reducer_spectral.fit_transform(data_train_trafo)\n", " \n", " print(\"Reduced Training Data\")\n", " plot_2d_data(reduced_train_data, categories, raw_data.train_labels)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "small_sample_categories = [\n", " 'comp.graphics',\n", " 'rec.autos',\n", " 'rec.sport.hockey',\n", " 'talk.politics.misc']" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (2243, 25740)\n", "Test data shape: (1494, 25740)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.01s.\n", "Scored accuracy on Test Data: 0.8949058159723067\n", "Confusion Matrix:\n", "[[364 15 9 1]\n", " [ 7 346 37 6]\n", " [ 2 4 390 3]\n", " [ 9 39 21 241]]\n", "Saving Confusion Matrix to outputdata/small_sample_no_stemming_MultinomialNB_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 14.15s.\n", "Scored accuracy on Test Data: 0.8464920129858231\n", "Confusion Matrix:\n", "[[340 35 8 6]\n", " [ 18 345 11 22]\n", " [ 10 25 348 16]\n", " [ 10 47 18 235]]\n", "Saving Confusion Matrix to outputdata/small_sample_no_stemming_MyClassifierNN_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 11.55s.\n", "Scored accuracy on Test Data: 0.8517850754645927\n", "Confusion Matrix:\n", "[[334 48 3 4]\n", " [ 6 375 6 9]\n", " [ 6 49 337 7]\n", " [ 6 74 5 225]]\n", "Saving Confusion Matrix to outputdata/small_sample_no_stemming_MyClassifierSVC_confusionmatrix.csv\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Training classifier ...\n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n", " n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n", "Training took 0.02s.\n", "Scored accuracy on Test Data: 0.8907818458884695\n", "Confusion Matrix:\n", "[[354 23 3 9]\n", " [ 8 366 11 11]\n", " [ 3 20 367 9]\n", " [ 4 48 11 247]]\n", "Saving Confusion Matrix to outputdata/small_sample_no_stemming_SGDClassifier_confusionmatrix.csv\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Training classifier ...\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)\n", "Training took 0.12s.\n", "Scored accuracy on Test Data: 0.9028941369462122\n", "Confusion Matrix:\n", "[[360 24 1 4]\n", " [ 9 373 4 10]\n", " [ 4 21 365 9]\n", " [ 7 46 4 253]]\n", "Saving Confusion Matrix to outputdata/small_sample_no_stemming_LogisticRegression_confusionmatrix.csv\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 0.02s.\n", "Scored accuracy on Test Data: 0.8622194929687326\n", "Confusion Matrix:\n", "[[336 43 1 9]\n", " [ 5 377 4 10]\n", " [ 4 38 349 8]\n", " [ 4 74 5 227]]\n", "Saving Confusion Matrix to outputdata/small_sample_no_stemming_PairwiseLearner_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 11.67s.\n", "Scored accuracy on Test Data: 0.8545701905554461\n", "Confusion Matrix:\n", "[[330 52 2 5]\n", " [ 3 384 1 8]\n", " [ 6 56 326 11]\n", " [ 4 73 0 233]]\n", "Saving Confusion Matrix to outputdata/small_sample_no_stemming_MyClassifierSVCnoFeatureWords_confusionmatrix.csv\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(small_sample_categories, stemming=False,\n", " vectorizer_params = {'ngram_range':(1,1), 'stop_words':'english'},\n", " cross_validate=False, run_name=\"small_sample_no_stemming\", consider_confusion_matrix = True)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (2243, 19179)\n", "Test data shape: (1494, 19179)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.01s.\n", "Scored accuracy on Test Data: 0.895032701501466\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 11.66s.\n", "Scored accuracy on Test Data: 0.8381823908524783\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 9.24s.\n", "Scored accuracy on Test Data: 0.8402963405841846\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Training classifier ...\n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n", " n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n", "Training took 0.01s.\n", "Scored accuracy on Test Data: 0.8864479897433235\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Training classifier ...\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)\n", "Training took 0.09s.\n", "Scored accuracy on Test Data: 0.9038753221418983\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 0.02s.\n", "Scored accuracy on Test Data: 0.8585183820883254\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 8.87s.\n", "Scored accuracy on Test Data: 0.854789753087245\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(small_sample_categories, stemming=True,\n", " vectorizer_params = {'ngram_range':(1,1), 'stop_words':'english'},\n", " cross_validate=False, run_name=\"small_sample_with_stemming\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (2243, 19366)\n", "Test data shape: (1494, 19366)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.01s.\n", "Scored accuracy on Test Data: 0.8831772876980928\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 12.85s.\n", "Scored accuracy on Test Data: 0.8240845062953744\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 9.96s.\n", "Scored accuracy on Test Data: 0.8302259578168629\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Training classifier ...\n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n", " n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n", "Training took 0.02s.\n", "Scored accuracy on Test Data: 0.8765744821185655\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Training classifier ...\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)\n", "Training took 0.14s.\n", "Scored accuracy on Test Data: 0.8867705240928974\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 0.02s.\n", "Scored accuracy on Test Data: 0.8477883199442624\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 9.67s.\n", "Scored accuracy on Test Data: 0.8407129581971045\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(small_sample_categories, stemming=True,\n", " vectorizer_params = {'ngram_range':(1,1)},\n", " cross_validate=False, run_name=\"small_sample_no_stop_words\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (2243, 186830)\n", "Test data shape: (1494, 186830)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.03s.\n", "Scored accuracy on Test Data: 0.8899975183879659\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 61.03s.\n", "Scored accuracy on Test Data: 0.8463058974994441\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 59.04s.\n", "Scored accuracy on Test Data: 0.858606513828617\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Training classifier ...\n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n", " n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n", "Training took 0.04s.\n", "Scored accuracy on Test Data: 0.913556186056186\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Training classifier ...\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)\n", "Training took 0.62s.\n", "Scored accuracy on Test Data: 0.9070045745559429\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 0.07s.\n", "Scored accuracy on Test Data: 0.8943433322481622\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 59.62s.\n", "Scored accuracy on Test Data: 0.8805278359221914\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(small_sample_categories, stemming=True,\n", " vectorizer_params = {'ngram_range':(1,2), 'stop_words':'english'},\n", " cross_validate=False, run_name=\"small_sample_twograms\")" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (2243, 19179)\n", "Test data shape: (1494, 19179)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.01s.\n", "Scored accuracy on Test Data: 0.895032701501466\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Running cross validation...\n", "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 162 out of 162 | elapsed: 64.9min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Datailed List of all CV scores:\n", " Score: 0.870708872152661 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.8609005794480503 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.8573339276424328 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.879179670297297 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.872046366473473 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.8689255463561467 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.8765046813367894 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.8765046813102157 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.8773963443679146 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.8845296476336925 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.884083816530021 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.8823004905209177 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.8644672312536678 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8707088719134983 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.8738296922434137 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.883192153339454 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8782880072130245 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.8800713332487013 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.8457423095930595 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.8488631296306649 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.8261257245824425 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.8769505129187861 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.8693713776724071 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.8528756129119845 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.8622380741408932 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.8559964334544888 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.8515381183520099 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.8782880073458926 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.8791796700315607 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.8675880517430248 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.8591172537046833 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8600089164966459 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.8559964333481944 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.8716005349977708 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8720463665797675 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.874275524064573 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.8386090059286773 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.824788230155336 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.80650913967812 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.8733838607677115 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.8600089163903514 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.8439589834776617 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.849308960973499 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.8533214444939813 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.8395006687737872 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.877842175551307 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.88274632199662 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.867142220240749 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.8591172534389471 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8644672311473732 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.861346410817458 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.8684797145349874 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8693713774863918 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.869817208962094 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 10}\n", "Best parms found by the cross validation :\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 100}\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=500, pca_dims=100, scale=False, svd_dims=200)\n", "Training took 31.04s.\n", "Scored accuracy on Test Data: 0.8909418472395005\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Running cross validation...\n", "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 162 out of 162 | elapsed: 10.3min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Datailed List of all CV scores:\n", " Score: 0.8502006241640659 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.8524297815425769 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.8555506018724922 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.26749888542131073 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.26749888542131073 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.26749888542131073 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.8631297369594293 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.8609005795809184 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.867142220240749 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.40971912617030765 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.4101649576460098 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.4190815871600535 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.8537672759696835 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8595630851538119 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.8613464110566206 for Parms:\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.8056174765938475 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.809184128399465 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.8056174765938475 for Parms:\n", " {'scale': False, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.8408381631743201 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.8412839946500222 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.8377173428444048 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.26749888542131073 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.26749888542131073 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.26749888542131073 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.8533214444939813 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.8466339723584485 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.8475256353098529 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.37316094516272846 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.3753901025412394 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.3896567097637093 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.8564422648238965 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8573339277753009 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.8506464556397682 for Parms:\n", " {'scale': True, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.8002674988854213 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8020508247882301 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.7980383415069104 for Parms:\n", " {'scale': False, 'n_feature_words': 200, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.8261257244761481 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.8292465448060633 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.8181007579135087 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.26749888542131073 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 200}\n", " Score: 0.26749888542131073 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 200}\n", " Score: 0.26749888542131073 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 200}\n", " Score: 0.838163174320107 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.8372715113687026 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.8345965225144896 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.3441818992420865 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 100}\n", " Score: 0.34195274186357555 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 100}\n", " Score: 0.35086937137761925 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 100}\n", " Score: 0.8484172982612572 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.8497547926883638 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.8475256353098529 for Parms:\n", " {'scale': True, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 10}\n", " Score: 0.7958091841283995 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 200, 'pca_dims': 10}\n", " Score: 0.7917967008470798 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 100, 'pca_dims': 10}\n", " Score: 0.7886758805171645 for Parms:\n", " {'scale': False, 'n_feature_words': 100, 'svd_dims': 10, 'pca_dims': 10}\n", "Best parms found by the cross validation :\n", " {'scale': True, 'n_feature_words': 500, 'svd_dims': 10, 'pca_dims': 100}\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=500, pca_dims=100, scale=True, svd_dims=10)\n", "Training took 1.76s.\n", "Scored accuracy on Test Data: 0.8786391548760655\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Running cross validation...\n", "Fitting 3 folds for each of 108 candidates, totalling 324 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 324 out of 324 | elapsed: 3.7min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Datailed List of all CV scores:\n", " Score: 0.8965670976370932 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.9059295586268391 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.8279090503789568 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.8970129291127954 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.8956754346856888 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.9054837271511369 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.8319215336602764 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.8983504235399019 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.8849754792688364 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.884083816317432 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.8203299152920196 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.8747213553276861 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.001}\n", " Score: 0.8974587605884975 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.9054837271511369 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.8288007133303611 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.8987962550156041 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.8921087828800713 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.9054837271511369 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.8345965225144896 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.8983504235399019 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.8675880517164511 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.8658047258136424 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.7909050378956755 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.8546589389210878 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.001}\n", " Score: 0.9086045474810521 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.9037004012483282 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.8399465002229157 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.8934462773071778 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.8858671422202408 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.9054837271511369 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.8403923316986179 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.8987962550156041 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.8675880517164511 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.8658047258136424 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.7891217119928667 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.8546589389210878 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", " Score: 0.9014712438698173 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.896121266161391 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.872046366473473 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.8952296032099867 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.8885421310744539 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.896121266161391 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.8675880517164511 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.8943379402585823 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.8845296477931341 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.8845296477931341 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.8787338386090058 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.884083816317432 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.1, 'alpha': 0.0001}\n", " Score: 0.8974587605884975 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8974587605884975 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8907712884529648 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8947837717342845 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8729380294248774 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.896121266161391 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8680338831921534 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8943379402585823 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8666963887650468 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8666963887650468 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8604547481052163 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.8644672313865359 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.01, 'alpha': 0.0001}\n", " Score: 0.9086045474810521 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.9086045474810521 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.903254569772626 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.9077128845296478 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.8880962995987517 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.896121266161391 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.8653588943379402 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.8956754346856888 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.8658047258136424 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.8680338831921534 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.8577797592510031 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.8649130628622381 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.0001}\n", " Score: 0.9045920641997325 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.896121266161391 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.8907712884529648 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.8987962550156041 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.8702630405706643 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.8956754346856888 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.8586714222024074 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.8898796255015604 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.884083816317432 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.8845296477931341 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.884083816317432 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.8849754792688364 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.1, 'alpha': 1e-05}\n", " Score: 0.8974587605884975 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8965670976370932 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8952296032099867 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8965670976370932 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8626839054837272 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8925546143557735 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8613464110566206 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8930004458314758 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8666963887650468 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8666963887650468 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8662505572893446 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.8662505572893446 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.01, 'alpha': 1e-05}\n", " Score: 0.9086045474810521 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.9086045474810521 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.9072670530539456 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.9086045474810521 for Parms:\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.8702630405706643 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.8907712884529648 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.8582255907267053 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.8979045920641997 for Parms:\n", " {'learning_rate': 'optimal', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.8666963887650468 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.8662505572893446 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l2', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.8658047258136424 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'l1', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", " Score: 0.8666963887650468 for Parms:\n", " {'learning_rate': 'invscaling', 'n_jobs': -1, 'penalty': 'elasticnet', 'max_iter': 500, 'eta0': 0.001, 'alpha': 1e-05}\n", "Best parms found by the cross validation :\n", " {'learning_rate': 'constant', 'n_jobs': -1, 'penalty': 'none', 'max_iter': 500, 'eta0': 0.001, 'alpha': 0.001}\n", "Training classifier ...\n", "SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.001, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='constant', loss='hinge', max_iter=500, n_iter=None,\n", " n_jobs=-1, penalty='none', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training took 0.5s.\n", "Scored accuracy on Test Data: 0.9132509854835295\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Running cross validation...\n", "Fitting 3 folds for each of 84 candidates, totalling 252 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 252 out of 252 | elapsed: 17.2min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Datailed List of all CV scores:\n", " Score: 0.8145341061078912 for Parms:\n", " {'multi_class': 'ovr', 'C': 0.1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.5283102987070887 for Parms:\n", " {'multi_class': 'ovr', 'C': 0.1, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8154257690592955 for Parms:\n", " {'multi_class': 'ovr', 'C': 0.1, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8439589835042354 for Parms:\n", " {'multi_class': 'multinomial', 'C': 0.1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.5278644672313866 for Parms:\n", " {'multi_class': 'multinomial', 'C': 0.1, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8444048149799376 for Parms:\n", " {'multi_class': 'multinomial', 'C': 0.1, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8987962550156041 for Parms:\n", " {'multi_class': 'ovr', 'C': 1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8189924208649131 for Parms:\n", " {'multi_class': 'ovr', 'C': 1, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8996879179670084 for Parms:\n", " {'multi_class': 'ovr', 'C': 1, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8996879179670084 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.814088274632189 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8992420864913063 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9041462327240303 for Parms:\n", " {'multi_class': 'ovr', 'C': 10, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8640213999108337 for Parms:\n", " {'multi_class': 'ovr', 'C': 10, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9041462327240303 for Parms:\n", " {'multi_class': 'ovr', 'C': 10, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.901025412394115 for Parms:\n", " {'multi_class': 'multinomial', 'C': 10, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8528756130182791 for Parms:\n", " {'multi_class': 'multinomial', 'C': 10, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9019170753455193 for Parms:\n", " {'multi_class': 'multinomial', 'C': 10, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9054837271511369 for Parms:\n", " {'multi_class': 'ovr', 'C': 100, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8827463218903254 for Parms:\n", " {'multi_class': 'ovr', 'C': 100, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9045920641997325 for Parms:\n", " {'multi_class': 'ovr', 'C': 100, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9023629068212216 for Parms:\n", " {'multi_class': 'multinomial', 'C': 100, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8769505127061971 for Parms:\n", " {'multi_class': 'multinomial', 'C': 100, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9023629068212216 for Parms:\n", " {'multi_class': 'multinomial', 'C': 100, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9059295586268391 for Parms:\n", " {'multi_class': 'ovr', 'C': 1000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.901025412394115 for Parms:\n", " {'multi_class': 'ovr', 'C': 1000, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9054837271511369 for Parms:\n", " {'multi_class': 'ovr', 'C': 1000, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9014712438698173 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8947837717342845 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1000, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9041462327240303 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1000, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9054837271511369 for Parms:\n", " {'multi_class': 'ovr', 'C': 2000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8987962550156041 for Parms:\n", " {'multi_class': 'ovr', 'C': 2000, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9072670530539456 for Parms:\n", " {'multi_class': 'ovr', 'C': 2000, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9045920641997325 for Parms:\n", " {'multi_class': 'multinomial', 'C': 2000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8965670976370932 for Parms:\n", " {'multi_class': 'multinomial', 'C': 2000, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.9041462327240303 for Parms:\n", " {'multi_class': 'multinomial', 'C': 2000, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", " Score: 0.8123049487293803 for Parms:\n", " {'multi_class': 'ovr', 'C': 0.1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.8127507802050825 for Parms:\n", " {'multi_class': 'ovr', 'C': 0.1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.8123049487293803 for Parms:\n", " {'multi_class': 'ovr', 'C': 0.1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.8421756576014267 for Parms:\n", " {'multi_class': 'multinomial', 'C': 0.1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.8421756576014267 for Parms:\n", " {'multi_class': 'multinomial', 'C': 0.1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.8421756576014267 for Parms:\n", " {'multi_class': 'multinomial', 'C': 0.1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.8983504235399019 for Parms:\n", " {'multi_class': 'ovr', 'C': 1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.8983504235399019 for Parms:\n", " {'multi_class': 'ovr', 'C': 1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.8983504235399019 for Parms:\n", " {'multi_class': 'ovr', 'C': 1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.8996879179670084 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.8996879179670084 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.8996879179670084 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.903254569772626 for Parms:\n", " {'multi_class': 'ovr', 'C': 10, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.903254569772626 for Parms:\n", " {'multi_class': 'ovr', 'C': 10, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.903254569772626 for Parms:\n", " {'multi_class': 'ovr', 'C': 10, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.9005795809184128 for Parms:\n", " {'multi_class': 'multinomial', 'C': 10, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.9005795809184128 for Parms:\n", " {'multi_class': 'multinomial', 'C': 10, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.9005795809184128 for Parms:\n", " {'multi_class': 'multinomial', 'C': 10, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.9037004012483282 for Parms:\n", " {'multi_class': 'ovr', 'C': 100, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.9041462327240303 for Parms:\n", " {'multi_class': 'ovr', 'C': 100, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.9037004012483282 for Parms:\n", " {'multi_class': 'ovr', 'C': 100, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.9019170753455193 for Parms:\n", " {'multi_class': 'multinomial', 'C': 100, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.9019170753455193 for Parms:\n", " {'multi_class': 'multinomial', 'C': 100, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.9019170753455193 for Parms:\n", " {'multi_class': 'multinomial', 'C': 100, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.9050378956754347 for Parms:\n", " {'multi_class': 'ovr', 'C': 1000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.9050378956754347 for Parms:\n", " {'multi_class': 'ovr', 'C': 1000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.9050378956754347 for Parms:\n", " {'multi_class': 'ovr', 'C': 1000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.9028087382969238 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.901025412394115 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.9028087382969238 for Parms:\n", " {'multi_class': 'multinomial', 'C': 1000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.9037004012483282 for Parms:\n", " {'multi_class': 'ovr', 'C': 2000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.9045920641997325 for Parms:\n", " {'multi_class': 'ovr', 'C': 2000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.9037004012483282 for Parms:\n", " {'multi_class': 'ovr', 'C': 2000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.9037004012483282 for Parms:\n", " {'multi_class': 'multinomial', 'C': 2000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'lbfgs'}\n", " Score: 0.9037004012483282 for Parms:\n", " {'multi_class': 'multinomial', 'C': 2000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'sag'}\n", " Score: 0.9041462327240303 for Parms:\n", " {'multi_class': 'multinomial', 'C': 2000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'newton-cg'}\n", " Score: 0.5278644672313866 for Parms:\n", " {'multi_class': 'ovr', 'C': 0.1, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.8221132411948283 for Parms:\n", " {'multi_class': 'ovr', 'C': 0.1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.8181007579135087 for Parms:\n", " {'multi_class': 'ovr', 'C': 1, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.8992420864913063 for Parms:\n", " {'multi_class': 'ovr', 'C': 1, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.8640213999108337 for Parms:\n", " {'multi_class': 'ovr', 'C': 10, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.903254569772626 for Parms:\n", " {'multi_class': 'ovr', 'C': 10, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.8631297369594293 for Parms:\n", " {'multi_class': 'ovr', 'C': 100, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.9037004012483282 for Parms:\n", " {'multi_class': 'ovr', 'C': 100, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.8680338831921534 for Parms:\n", " {'multi_class': 'ovr', 'C': 1000, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.9050378956754347 for Parms:\n", " {'multi_class': 'ovr', 'C': 1000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.8760588497547926 for Parms:\n", " {'multi_class': 'ovr', 'C': 2000, 'penalty': 'l1', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", " Score: 0.9045920641997325 for Parms:\n", " {'multi_class': 'ovr', 'C': 2000, 'penalty': 'l2', 'n_jobs': -1, 'max_iter': 500, 'solver': 'liblinear'}\n", "Best parms found by the cross validation :\n", " {'multi_class': 'ovr', 'C': 2000, 'penalty': 'multinomial', 'n_jobs': -1, 'max_iter': 500, 'solver': 'saga'}\n", "Training classifier ...\n", "LogisticRegression(C=2000, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=500, multi_class='ovr', n_jobs=-1,\n", " penalty='multinomial', random_state=None, solver='saga',\n", " tol=0.0001, verbose=0, warm_start=False)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training took 15.83s.\n", "Scored accuracy on Test Data: 0.8937752588972501\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 0.03s.\n", "Scored accuracy on Test Data: 0.8671329264629438\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 7.57s.\n", "Scored accuracy on Test Data: 0.8593440296308799\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(small_sample_categories, vectorizer_params = {'ngram_range':(1,1), 'stop_words':'english'},\n", " cross_validate=True, print_all_cv_scores=True, consider_confusion_matrix=False, run_name=\"small_sample_crossvalidate\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "all_categories = ['alt.atheism',\n", " 'comp.graphics',\n", " 'comp.os.ms-windows.misc',\n", " 'comp.sys.ibm.pc.hardware',\n", " 'comp.sys.mac.hardware',\n", " 'comp.windows.x',\n", " 'misc.forsale',\n", " 'rec.autos',\n", " 'rec.motorcycles',\n", " 'rec.sport.baseball',\n", " 'rec.sport.hockey',\n", " 'sci.crypt',\n", " 'sci.electronics',\n", " 'sci.med',\n", " 'sci.space',\n", " 'soc.religion.christian',\n", " 'talk.politics.guns',\n", " 'talk.politics.mideast',\n", " 'talk.politics.misc',\n", " 'talk.religion.misc']" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (11314, 84284)\n", "Test data shape: (7532, 84284)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.08s.\n", "Scored accuracy on Test Data: 0.6437487066768769\n", "Confusion Matrix:\n", "[[ 52 0 0 1 0 3 0 1 3 3 11 7 1 3 8 202 9 14\n", " 1 0]\n", " [ 0 270 17 12 12 25 6 0 4 0 7 18 2 0 9 6 0 1\n", " 0 0]\n", " [ 1 27 219 46 10 25 5 1 2 0 15 26 0 0 6 9 1 1\n", " 0 0]\n", " [ 0 9 31 284 21 2 10 0 0 0 8 8 15 0 0 3 1 0\n", " 0 0]\n", " [ 0 8 16 35 263 4 8 2 1 0 14 14 10 1 3 4 2 0\n", " 0 0]\n", " [ 0 33 13 10 3 300 2 1 0 0 8 11 4 0 3 6 1 0\n", " 0 0]\n", " [ 0 2 2 39 12 1 285 12 4 2 9 2 6 1 3 5 4 1\n", " 0 0]\n", " [ 0 0 1 1 0 1 8 298 10 2 28 7 12 2 6 10 6 2\n", " 2 0]\n", " [ 0 2 0 3 1 1 9 16 290 3 19 6 10 3 4 18 7 4\n", " 2 0]\n", " [ 0 4 1 2 0 1 6 0 2 303 43 5 1 4 0 20 3 2\n", " 0 0]\n", " [ 0 1 1 0 0 0 0 1 0 2 372 4 0 3 1 11 3 0\n", " 0 0]\n", " [ 0 3 9 2 1 2 2 2 1 0 18 319 3 3 4 11 12 3\n", " 1 0]\n", " [ 0 11 7 31 14 2 16 12 5 1 11 52 201 9 11 6 2 2\n", " 0 0]\n", " [ 0 11 0 0 1 0 0 3 2 1 18 4 6 307 3 32 5 2\n", " 1 0]\n", " [ 0 11 2 1 1 0 3 2 2 1 19 10 5 5 300 23 4 3\n", " 2 0]\n", " [ 2 4 1 0 0 0 1 0 0 0 14 3 0 2 1 368 1 1\n", " 0 0]\n", " [ 0 1 2 0 0 1 1 2 3 0 13 29 0 2 4 33 261 10\n", " 2 0]\n", " [ 2 1 0 0 0 1 0 0 5 2 7 9 0 1 0 39 9 297\n", " 3 0]\n", " [ 1 1 0 0 1 2 0 4 1 1 9 19 2 5 11 49 103 12\n", " 89 0]\n", " [ 8 4 2 0 0 0 1 2 3 1 8 5 0 4 4 167 31 9\n", " 0 2]]\n", "Saving Confusion Matrix to outputdata/full_set_MultinomialNB_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 81.38s.\n", "Scored accuracy on Test Data: 0.6397794558049733\n", "Confusion Matrix:\n", "[[143 3 2 0 1 1 0 14 5 6 1 5 4 9 14 63 10 10\n", " 9 19]\n", " [ 4 256 17 10 8 27 8 8 5 2 0 4 21 6 6 0 1 3\n", " 1 2]\n", " [ 2 30 239 31 17 18 8 19 4 1 1 2 6 2 5 0 0 2\n", " 5 2]\n", " [ 0 14 31 239 30 5 18 12 0 1 0 3 34 1 1 1 1 1\n", " 0 0]\n", " [ 1 11 10 40 246 8 15 17 5 3 0 2 15 4 6 0 0 0\n", " 0 2]\n", " [ 0 52 26 5 7 263 3 14 4 2 0 1 7 2 4 0 0 1\n", " 4 0]\n", " [ 0 2 4 21 11 2 300 18 4 4 3 0 8 3 3 2 3 1\n", " 1 0]\n", " [ 3 2 0 2 1 1 15 292 24 5 2 3 24 4 6 2 2 1\n", " 5 2]\n", " [ 5 2 2 1 4 1 7 45 277 6 5 2 10 7 7 1 5 3\n", " 7 1]\n", " [ 2 3 0 1 1 1 6 23 4 312 18 0 1 5 4 3 3 4\n", " 6 0]\n", " [ 2 1 0 0 0 0 0 15 3 16 342 1 1 3 6 3 3 1\n", " 2 0]\n", " [ 4 2 2 4 2 4 6 20 5 2 3 272 17 3 7 0 24 6\n", " 11 2]\n", " [ 5 14 6 31 19 0 22 27 12 2 0 10 208 16 11 2 2 3\n", " 2 1]\n", " [ 7 9 1 1 0 0 3 27 3 0 2 2 11 303 3 2 3 1\n", " 14 4]\n", " [ 10 14 3 1 1 1 5 25 5 5 1 8 16 14 265 3 3 2\n", " 10 2]\n", " [ 38 4 1 0 1 1 1 15 3 2 0 3 1 3 3 296 2 3\n", " 4 17]\n", " [ 10 6 4 0 2 1 1 16 6 3 1 13 6 9 6 7 226 7\n", " 26 14]\n", " [ 34 1 1 0 0 1 1 7 5 8 1 1 1 7 2 7 15 259\n", " 22 3]\n", " [ 13 1 1 0 0 1 1 15 4 1 4 3 5 10 9 2 85 11\n", " 132 12]\n", " [ 40 1 1 1 1 1 3 13 4 4 0 0 0 9 3 83 27 7\n", " 7 46]]\n", "Saving Confusion Matrix to outputdata/full_set_MyClassifierNN_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 85.13s.\n", "Scored accuracy on Test Data: 0.6263700623244223\n", "Confusion Matrix:\n", "[[151 2 2 0 1 2 1 7 5 9 0 4 4 7 33 45 6 8\n", " 6 26]\n", " [ 6 264 20 12 6 21 4 7 3 4 0 2 16 2 19 0 0 1\n", " 2 0]\n", " [ 3 28 242 30 14 16 5 5 6 1 0 0 5 5 27 0 0 2\n", " 3 2]\n", " [ 0 22 44 230 22 3 15 2 0 1 0 4 33 3 13 0 0 0\n", " 0 0]\n", " [ 3 11 17 34 232 3 12 7 2 1 0 0 30 7 24 0 0 0\n", " 1 1]\n", " [ 0 47 36 9 5 239 1 3 10 2 0 2 11 3 20 0 1 2\n", " 4 0]\n", " [ 0 2 5 19 11 1 298 17 5 3 1 0 11 2 13 0 1 1\n", " 0 0]\n", " [ 3 4 1 5 0 0 11 266 19 3 3 2 25 6 38 1 3 1\n", " 4 1]\n", " [ 6 2 2 1 0 0 9 36 270 7 2 1 16 5 24 2 4 4\n", " 7 0]\n", " [ 6 2 1 2 1 0 9 5 7 302 11 0 6 2 29 1 3 3\n", " 5 2]\n", " [ 3 3 1 0 0 0 1 4 5 19 327 1 4 5 17 0 3 2\n", " 3 1]\n", " [ 6 7 7 5 4 1 4 7 7 3 3 248 21 7 27 2 22 4\n", " 11 0]\n", " [ 7 16 8 25 20 3 17 20 7 4 0 7 213 14 26 0 0 4\n", " 1 1]\n", " [ 8 4 1 1 0 0 4 12 3 1 3 0 11 297 34 3 2 2\n", " 10 0]\n", " [ 7 9 3 1 2 1 6 11 7 4 0 2 20 12 292 2 4 2\n", " 8 1]\n", " [ 49 2 2 0 0 2 3 1 7 1 0 1 5 5 19 263 1 4\n", " 12 21]\n", " [ 14 3 6 0 1 0 1 10 11 3 0 5 6 6 23 4 230 9\n", " 23 9]\n", " [ 38 2 1 0 0 1 2 8 5 4 0 1 7 3 13 6 9 250\n", " 24 2]\n", " [ 15 0 2 0 0 0 3 8 6 2 1 3 4 9 19 0 87 13\n", " 130 8]\n", " [ 44 3 3 1 0 1 0 6 7 4 0 1 2 7 21 67 27 10\n", " 3 44]]\n", "Saving Confusion Matrix to outputdata/full_set_MyClassifierSVC_confusionmatrix.csv\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Training classifier ...\n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n", " n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n", "Training took 0.36s.\n", "Scored accuracy on Test Data: 0.6841423775548515\n", "Confusion Matrix:\n", "[[161 1 1 2 1 2 4 3 1 14 4 3 6 7 15 51 6 9\n", " 6 22]\n", " [ 4 284 21 6 4 17 6 1 2 7 1 10 9 4 8 1 1 1\n", " 1 1]\n", " [ 1 24 228 38 17 20 9 2 3 17 3 5 1 6 8 0 0 2\n", " 8 2]\n", " [ 1 12 33 264 22 4 15 0 0 8 1 4 24 1 1 0 0 0\n", " 2 0]\n", " [ 1 4 10 25 277 8 13 4 1 14 1 4 12 3 4 0 2 1\n", " 0 1]\n", " [ 1 41 29 5 8 284 1 1 1 8 1 2 7 0 5 0 0 1\n", " 0 0]\n", " [ 2 3 3 18 16 0 302 8 4 10 1 1 9 2 2 1 4 3\n", " 0 1]\n", " [ 0 2 3 1 2 0 12 288 14 29 2 4 18 3 7 0 4 2\n", " 4 1]\n", " [ 0 1 1 1 1 1 5 18 307 20 1 0 9 7 8 3 3 2\n", " 7 3]\n", " [ 1 0 1 1 0 1 4 2 5 348 13 0 2 5 3 3 1 1\n", " 6 0]\n", " [ 1 0 0 1 1 0 1 2 2 20 361 0 1 2 1 1 4 0\n", " 1 0]\n", " [ 5 5 4 3 4 3 9 5 2 20 2 288 9 4 6 0 15 3\n", " 8 1]\n", " [ 6 9 11 27 17 5 21 8 13 16 3 11 215 11 11 1 1 3\n", " 3 1]\n", " [ 6 7 0 1 1 0 2 2 3 16 7 1 10 319 3 5 5 1\n", " 6 1]\n", " [ 2 13 1 0 1 3 5 9 4 19 1 4 11 7 301 4 2 0\n", " 6 1]\n", " [ 25 2 2 0 1 0 3 0 1 16 1 1 1 5 5 317 0 4\n", " 5 9]\n", " [ 11 3 3 2 2 0 2 6 9 18 1 10 1 5 7 6 237 7\n", " 26 8]\n", " [ 31 1 1 1 0 2 2 2 4 9 0 2 3 3 2 11 10 279\n", " 12 1]\n", " [ 11 0 0 1 0 3 1 6 6 12 2 4 3 8 14 2 83 5\n", " 140 9]\n", " [ 35 3 2 2 0 2 2 4 4 8 1 0 3 11 5 71 23 8\n", " 5 62]]\n", "Saving Confusion Matrix to outputdata/full_set_SGDClassifier_confusionmatrix.csv\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Training classifier ...\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training took 4.59s.\n", "Scored accuracy on Test Data: 0.6731765174891535\n", "Confusion Matrix:\n", "[[154 2 1 1 0 1 3 2 15 7 1 1 5 9 13 71 8 9\n", " 5 11]\n", " [ 4 272 21 8 7 21 6 1 8 4 0 5 17 3 8 1 1 1\n", " 1 0]\n", " [ 3 28 242 31 15 21 7 1 19 2 2 3 3 4 6 1 0 1\n", " 4 1]\n", " [ 0 12 34 253 27 6 15 1 8 1 1 1 30 1 1 0 1 0\n", " 0 0]\n", " [ 2 6 11 25 269 4 10 2 17 2 2 2 22 3 5 0 1 1\n", " 0 1]\n", " [ 0 40 33 5 7 275 2 3 10 2 0 3 7 0 4 0 1 2\n", " 1 0]\n", " [ 1 2 3 20 11 0 304 12 12 3 1 1 11 2 3 2 0 1\n", " 1 0]\n", " [ 1 3 1 0 2 1 14 287 41 5 1 1 22 3 4 1 4 1\n", " 4 0]\n", " [ 3 0 0 3 1 0 10 20 315 8 0 0 12 5 6 3 3 1\n", " 7 1]\n", " [ 3 3 1 1 0 1 5 2 21 319 25 0 2 4 1 2 0 2\n", " 5 0]\n", " [ 3 1 0 0 0 0 0 2 13 10 353 0 3 3 2 1 6 0\n", " 2 0]\n", " [ 5 7 5 3 3 2 7 2 21 5 2 270 17 8 7 2 18 4\n", " 8 0]\n", " [ 3 15 11 19 13 2 23 11 18 4 0 10 238 13 8 1 1 2\n", " 1 0]\n", " [ 6 7 0 1 1 0 4 7 19 0 2 0 11 318 4 5 4 1\n", " 5 1]\n", " [ 7 11 2 0 2 2 4 5 24 4 0 1 18 12 289 2 3 0\n", " 7 1]\n", " [ 22 3 3 0 0 0 3 0 17 3 0 1 2 5 5 314 1 2\n", " 8 9]\n", " [ 12 2 3 1 2 1 1 4 20 6 0 6 3 7 4 9 243 7\n", " 27 6]\n", " [ 24 4 1 0 0 1 0 0 14 6 0 1 4 3 1 9 6 284\n", " 17 1]\n", " [ 13 1 0 0 0 1 1 6 14 4 2 2 4 9 12 2 85 7\n", " 140 7]\n", " [ 40 4 0 1 0 0 1 3 14 6 2 0 3 10 6 78 25 10\n", " 5 43]]\n", "Saving Confusion Matrix to outputdata/full_set_LogisticRegression_confusionmatrix.csv\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 1.4s.\n", "Scored accuracy on Test Data: 0.6456120003282815\n", "Confusion Matrix:\n", "[[149 5 2 0 1 0 0 22 6 9 0 2 4 2 14 44 9 8\n", " 4 38]\n", " [ 5 264 17 13 10 20 5 16 4 5 1 5 13 3 7 1 0 0\n", " 0 0]\n", " [ 3 29 230 40 15 11 3 27 5 2 1 9 5 3 7 0 0 1\n", " 2 1]\n", " [ 1 24 31 248 27 5 17 11 0 0 1 2 19 2 2 0 1 0\n", " 1 0]\n", " [ 2 7 11 31 259 1 12 25 1 2 2 2 20 4 4 0 1 0\n", " 0 1]\n", " [ 0 51 39 5 7 253 2 17 1 4 0 3 9 0 4 0 0 0\n", " 0 0]\n", " [ 0 6 3 20 17 1 289 26 6 3 0 1 10 1 1 1 1 4\n", " 0 0]\n", " [ 3 1 0 0 6 2 15 324 10 2 3 0 12 3 4 1 2 3\n", " 3 2]\n", " [ 4 1 2 2 3 1 5 65 276 6 1 2 12 1 3 0 5 1\n", " 6 2]\n", " [ 6 9 0 0 1 1 7 31 4 304 19 1 1 2 1 1 2 2\n", " 5 0]\n", " [ 4 2 1 1 2 0 0 30 1 23 322 0 0 4 1 0 2 1\n", " 1 4]\n", " [ 5 8 5 6 3 0 4 28 5 6 3 262 17 3 9 2 10 4\n", " 13 3]\n", " [ 2 17 11 22 22 3 18 38 11 4 1 9 216 7 6 1 0 3\n", " 1 1]\n", " [ 5 6 2 1 3 0 6 42 5 2 2 1 17 279 6 1 4 4\n", " 4 6]\n", " [ 6 13 2 0 5 1 2 39 9 2 0 1 17 9 270 3 4 1\n", " 8 2]\n", " [ 30 3 2 0 1 0 1 27 4 5 0 0 4 7 5 257 0 3\n", " 2 47]\n", " [ 11 2 5 0 1 0 2 36 8 4 0 11 0 5 7 6 225 6\n", " 20 15]\n", " [ 27 1 2 1 0 1 0 15 5 8 0 2 2 3 3 10 3 268\n", " 18 7]\n", " [ 16 1 2 0 0 1 1 21 6 4 1 6 4 4 10 5 79 5\n", " 130 14]\n", " [ 36 3 0 1 2 0 1 21 5 5 2 3 3 4 5 48 21 7\n", " 9 75]]\n", "Saving Confusion Matrix to outputdata/full_set_PairwiseLearner_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 80.66s.\n", "Scored accuracy on Test Data: 0.6085327723200893\n", "Confusion Matrix:\n", "[[144 2 1 0 2 1 5 17 14 6 0 2 3 8 17 51 3 15\n", " 11 17]\n", " [ 5 248 25 16 8 23 6 15 4 4 0 5 14 3 11 0 0 0\n", " 1 1]\n", " [ 5 28 223 30 21 20 5 24 2 5 0 2 5 4 11 0 0 2\n", " 2 5]\n", " [ 1 22 35 237 21 3 17 11 1 4 0 3 24 1 8 0 1 2\n", " 1 0]\n", " [ 2 10 16 46 227 0 8 28 4 7 0 3 21 5 3 1 2 1\n", " 0 1]\n", " [ 3 42 27 11 6 238 1 19 4 3 1 3 18 1 8 0 1 4\n", " 5 0]\n", " [ 3 7 5 20 12 0 295 25 3 5 1 0 6 0 5 1 1 0\n", " 1 0]\n", " [ 2 1 2 3 2 0 10 293 24 8 1 2 24 8 6 1 2 2\n", " 4 1]\n", " [ 6 4 2 2 1 2 10 61 259 10 0 1 13 5 5 2 4 3\n", " 8 0]\n", " [ 3 4 3 2 0 1 6 26 7 305 17 1 1 4 4 0 2 3\n", " 6 2]\n", " [ 5 3 0 2 0 0 1 20 10 22 316 1 3 5 3 0 2 3\n", " 3 0]\n", " [ 6 7 10 7 8 2 5 25 11 2 3 231 23 5 7 1 21 3\n", " 17 2]\n", " [ 1 21 9 29 10 1 15 40 11 4 0 7 212 10 13 2 0 5\n", " 1 2]\n", " [ 8 6 4 2 1 0 6 28 7 2 3 1 13 289 7 3 2 3\n", " 10 1]\n", " [ 9 11 2 1 3 2 3 28 8 6 0 2 19 15 268 0 4 0\n", " 10 3]\n", " [ 33 4 4 0 0 1 3 19 9 1 0 1 4 6 4 272 0 5\n", " 15 17]\n", " [ 16 2 3 1 1 1 3 29 13 11 1 10 2 5 4 4 213 14\n", " 22 9]\n", " [ 29 1 4 2 1 2 1 13 6 8 0 1 4 7 1 7 8 261\n", " 18 2]\n", " [ 15 2 2 0 0 0 2 18 10 5 3 6 3 12 11 1 86 12\n", " 115 7]\n", " [ 50 3 1 2 0 0 1 18 7 6 1 0 3 9 7 68 30 8\n", " 5 32]]\n", "Saving Confusion Matrix to outputdata/full_set_MyClassifierSVCnoFeatureWords_confusionmatrix.csv\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(all_categories, vectorizer_params = {'ngram_range':(1,1), 'stop_words':'english'},\n", " cross_validate=False, consider_confusion_matrix=True, run_name=\"full_set\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "religious_categories = ['alt.atheism',\n", " 'soc.religion.christian',\n", " 'talk.religion.misc']" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (1456, 12661)\n", "Test data shape: (968, 12661)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.0s.\n", "Scored accuracy on Test Data: 0.38478076316402055\n", "Confusion Matrix:\n", "[[ 99 218 2]\n", " [ 4 394 0]\n", " [ 24 218 9]]\n", "Saving Confusion Matrix to outputdata/religious_set_MultinomialNB_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 14.58s.\n", "Scored accuracy on Test Data: 0.5823555242838238\n", "Confusion Matrix:\n", "[[168 71 80]\n", " [ 39 291 68]\n", " [ 64 64 123]]\n", "Saving Confusion Matrix to outputdata/religious_set_MyClassifierNN_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 5.33s.\n", "Scored accuracy on Test Data: 0.5664044430029086\n", "Confusion Matrix:\n", "[[166 89 64]\n", " [ 42 311 45]\n", " [ 65 86 100]]\n", "Saving Confusion Matrix to outputdata/religious_set_MyClassifierSVC_confusionmatrix.csv\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Training classifier ...\n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n", " n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n", "Training took 0.01s.\n", "Scored accuracy on Test Data: 0.5996811305660102\n", "Confusion Matrix:\n", "[[181 69 69]\n", " [ 29 311 58]\n", " [ 53 86 112]]\n", "Saving Confusion Matrix to outputdata/religious_set_SGDClassifier_confusionmatrix.csv\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Training classifier ...\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)\n", "Training took 0.06s.\n", "Scored accuracy on Test Data: 0.5662296879102624\n", "Confusion Matrix:\n", "[[203 94 22]\n", " [ 47 339 12]\n", " [ 80 110 61]]\n", "Saving Confusion Matrix to outputdata/religious_set_LogisticRegression_confusionmatrix.csv\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 0.01s.\n", "Scored accuracy on Test Data: 0.5800985812841994\n", "Confusion Matrix:\n", "[[183 55 81]\n", " [ 45 272 81]\n", " [ 61 69 121]]\n", "Saving Confusion Matrix to outputdata/religious_set_PairwiseLearner_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 4.87s.\n", "Scored accuracy on Test Data: 0.6050864712631348\n", "Confusion Matrix:\n", "[[153 40 126]\n", " [ 34 275 89]\n", " [ 33 51 167]]\n", "Saving Confusion Matrix to outputdata/religious_set_MyClassifierSVCnoFeatureWords_confusionmatrix.csv\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(religious_categories, vectorizer_params = {'ngram_range':(1,1), 'stop_words':'english'},\n", " cross_validate=False, print_all_cv_scores=False, consider_confusion_matrix=True, run_name=\"religious_set\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "two_religious_categories = ['soc.religion.christian',\n", " 'talk.religion.misc']" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (976, 10598)\n", "Test data shape: (649, 10598)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.0s.\n", "Scored accuracy on Test Data: 0.45369778288373236\n", "Confusion Matrix:\n", "[[398 0]\n", " [233 18]]\n", "Saving Confusion Matrix to outputdata/two_rel_cats_MultinomialNB_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 12.58s.\n", "Scored accuracy on Test Data: 0.6770581113801453\n", "Confusion Matrix:\n", "[[316 82]\n", " [112 139]]\n", "Saving Confusion Matrix to outputdata/two_rel_cats_MyClassifierNN_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 3.6s.\n", "Scored accuracy on Test Data: 0.6296478573434259\n", "Confusion Matrix:\n", "[[380 18]\n", " [171 80]]\n", "Saving Confusion Matrix to outputdata/two_rel_cats_MyClassifierSVC_confusionmatrix.csv\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Training classifier ...\n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n", " n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n", "Training took 0.0s.\n", "Scored accuracy on Test Data: 0.7338591393385914\n", "Confusion Matrix:\n", "[[320 78]\n", " [ 85 166]]\n", "Saving Confusion Matrix to outputdata/two_rel_cats_SGDClassifier_confusionmatrix.csv\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Training classifier ...\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)\n", "Training took 0.01s.\n", "Scored accuracy on Test Data: 0.6593907297406905\n", "Confusion Matrix:\n", "[[383 15]\n", " [161 90]]\n", "Saving Confusion Matrix to outputdata/two_rel_cats_LogisticRegression_confusionmatrix.csv\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 0.0s.\n", "Scored accuracy on Test Data: 0.7222551092318534\n", "Confusion Matrix:\n", "[[329 69]\n", " [ 98 153]]\n", "Saving Confusion Matrix to outputdata/two_rel_cats_PairwiseLearner_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 3.41s.\n", "Scored accuracy on Test Data: 0.7463354318204725\n", "Confusion Matrix:\n", "[[305 93]\n", " [ 66 185]]\n", "Saving Confusion Matrix to outputdata/two_rel_cats_MyClassifierSVCnoFeatureWords_confusionmatrix.csv\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(two_religious_categories, vectorizer_params = {'ngram_range':(1,1), 'stop_words':'english'},\n", " cross_validate=False, print_all_cv_scores=False, consider_confusion_matrix=True, run_name=\"two_rel_cats\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "simplyfied_categories = [\n", " 'comp.graphics',\n", " 'comp.os.ms-windows.misc',\n", " 'comp.sys.ibm.pc.hardware',\n", " 'comp.sys.mac.hardware',\n", " 'comp.windows.x',\n", " 'misc.forsale',\n", " 'rec.autos',\n", " 'rec.motorcycles',\n", " 'rec.sport.baseball',\n", " 'rec.sport.hockey',\n", " 'sci.electronics',\n", " 'sci.med',\n", " 'sci.space',\n", " 'talk.politics.misc',\n", " 'talk.religion.misc']" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (8530, 71079)\n", "Test data shape: (5679, 71079)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.05s.\n", "Scored accuracy on Test Data: 0.7227304336167872\n", "Confusion Matrix:\n", "[[277 17 13 12 27 6 2 4 1 7 3 3 17 0 0]\n", " [ 30 223 52 10 32 4 1 4 0 16 0 9 11 1 1]\n", " [ 7 34 285 23 4 10 2 0 0 8 17 1 1 0 0]\n", " [ 10 17 37 263 5 10 5 1 0 15 14 2 6 0 0]\n", " [ 35 14 11 3 306 2 2 1 0 11 4 0 5 1 0]\n", " [ 2 2 39 11 1 287 15 5 3 10 8 2 5 0 0]\n", " [ 0 1 1 1 2 9 301 14 4 31 14 3 10 4 1]\n", " [ 3 0 4 2 1 8 19 308 5 18 10 9 5 6 0]\n", " [ 6 1 2 0 2 6 0 2 317 47 1 7 2 4 0]\n", " [ 1 1 1 0 1 0 2 1 3 378 0 7 1 3 0]\n", " [ 16 14 39 15 3 15 13 8 3 11 222 15 18 1 0]\n", " [ 12 0 0 2 0 0 4 5 2 18 6 333 5 6 3]\n", " [ 13 2 1 1 3 3 5 4 1 21 5 9 316 10 0]\n", " [ 1 1 1 1 3 2 15 8 2 15 2 24 19 203 13]\n", " [ 4 3 3 0 2 1 9 10 6 13 3 39 14 28 116]]\n", "Saving Confusion Matrix to outputdata/reduced_fulls_set_MultinomialNB_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 60.04s.\n", "Scored accuracy on Test Data: 0.6847374000318412\n", "Confusion Matrix:\n", "[[257 18 10 13 37 8 10 4 2 1 14 5 5 1 4]\n", " [ 28 232 32 16 25 8 20 4 1 2 5 4 9 3 5]\n", " [ 14 38 246 31 2 17 11 0 0 2 26 2 1 2 0]\n", " [ 10 13 35 246 5 17 19 2 2 2 19 4 8 2 1]\n", " [ 40 33 6 7 277 3 11 2 0 3 9 0 3 1 0]\n", " [ 1 4 16 16 2 304 17 5 5 1 6 5 3 2 3]\n", " [ 4 3 1 2 2 13 294 31 2 4 12 7 7 7 7]\n", " [ 2 2 3 2 0 6 48 284 6 7 10 5 9 9 5]\n", " [ 3 1 3 0 2 10 24 2 298 22 4 5 4 12 7]\n", " [ 1 0 1 2 0 1 14 4 13 346 1 6 3 5 2]\n", " [ 11 9 39 28 2 23 32 14 6 2 197 15 8 6 1]\n", " [ 5 2 2 1 2 1 27 7 1 4 7 310 3 15 9]\n", " [ 10 3 1 3 3 7 24 11 8 5 17 15 267 16 4]\n", " [ 1 4 1 0 1 0 26 13 0 5 4 9 13 174 59]\n", " [ 3 2 0 1 3 4 15 8 8 1 3 13 5 17 168]]\n", "Saving Confusion Matrix to outputdata/reduced_fulls_set_MyClassifierNN_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 56.15s.\n", "Scored accuracy on Test Data: 0.6777225701602532\n", "Confusion Matrix:\n", "[[260 23 14 7 20 6 14 3 3 1 17 3 14 2 2]\n", " [ 26 234 30 14 20 4 21 4 1 0 7 10 14 6 3]\n", " [ 15 43 238 18 2 17 13 1 3 0 32 6 4 0 0]\n", " [ 7 19 37 230 3 13 24 1 1 1 29 7 11 0 2]\n", " [ 48 35 8 4 244 3 19 3 2 0 14 2 10 3 0]\n", " [ 1 6 15 10 1 304 22 6 4 1 11 3 6 0 0]\n", " [ 3 4 3 0 0 10 309 18 2 2 20 5 11 7 2]\n", " [ 2 2 1 1 1 8 62 269 6 2 14 7 10 11 2]\n", " [ 2 2 2 0 0 8 29 5 300 11 5 7 10 13 3]\n", " [ 3 1 1 0 2 1 19 3 19 326 3 4 10 5 2]\n", " [ 13 13 31 14 3 17 36 6 5 0 219 14 18 2 2]\n", " [ 7 1 1 1 0 3 33 1 1 4 12 299 16 12 5]\n", " [ 10 3 1 2 3 6 31 5 5 0 21 15 276 12 4]\n", " [ 0 2 0 0 0 2 25 9 0 2 6 13 22 171 58]\n", " [ 4 1 1 1 2 1 24 6 6 0 0 19 14 12 160]]\n", "Saving Confusion Matrix to outputdata/reduced_fulls_set_MyClassifierSVC_confusionmatrix.csv\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Training classifier ...\n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n", " n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n", "Training took 0.18s.\n", "Scored accuracy on Test Data: 0.7234569189268245\n", "Confusion Matrix:\n", "[[284 23 7 7 18 9 2 7 3 1 12 3 10 2 1]\n", " [ 25 240 29 18 18 8 1 20 2 3 2 5 9 9 5]\n", " [ 12 43 244 24 7 14 1 7 0 1 33 1 2 3 0]\n", " [ 4 14 24 279 6 11 5 14 0 2 15 2 5 2 2]\n", " [ 42 33 5 8 280 1 0 10 1 1 8 0 5 1 0]\n", " [ 5 5 14 17 0 306 10 14 3 1 8 1 4 0 2]\n", " [ 2 7 1 3 0 17 283 40 3 2 18 2 8 7 3]\n", " [ 1 2 3 1 0 6 21 312 5 1 13 8 6 16 3]\n", " [ 1 1 1 1 2 10 4 23 320 15 2 7 3 7 0]\n", " [ 0 1 0 1 0 1 2 14 10 360 0 5 2 1 2]\n", " [ 11 14 24 19 5 18 8 23 4 3 232 11 13 4 4]\n", " [ 5 1 1 1 1 4 4 20 1 6 12 321 4 6 9]\n", " [ 12 4 0 1 2 5 5 25 4 1 11 12 299 9 4]\n", " [ 0 1 3 2 1 2 8 16 4 3 2 7 12 183 66]\n", " [ 3 2 3 3 1 2 5 14 1 3 5 9 5 18 177]]\n", "Saving Confusion Matrix to outputdata/reduced_fulls_set_SGDClassifier_confusionmatrix.csv\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Training classifier ...\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)\n", "Training took 2.45s.\n", "Scored accuracy on Test Data: 0.7224709221913589\n", "Confusion Matrix:\n", "[[276 22 9 7 19 8 1 8 4 0 18 3 10 3 1]\n", " [ 27 242 29 15 23 6 2 19 3 2 5 4 6 7 4]\n", " [ 12 35 251 27 6 14 2 8 2 1 31 1 1 1 0]\n", " [ 6 11 22 266 5 13 3 18 2 2 24 4 8 0 1]\n", " [ 43 33 5 6 277 2 3 9 1 0 8 0 4 4 0]\n", " [ 2 3 20 11 0 306 10 13 5 1 12 2 3 2 0]\n", " [ 1 1 0 2 2 13 289 41 6 1 22 4 5 7 2]\n", " [ 0 0 3 1 0 9 21 319 8 1 12 7 7 7 3]\n", " [ 2 1 0 0 1 7 2 21 316 25 3 6 4 9 0]\n", " [ 1 0 0 0 0 1 2 16 10 355 3 3 2 5 1]\n", " [ 15 13 20 13 3 20 12 21 6 0 241 14 12 1 2]\n", " [ 7 0 1 2 1 3 5 21 1 2 11 322 4 9 7]\n", " [ 13 2 0 2 2 4 6 26 3 0 17 13 288 15 3]\n", " [ 1 1 0 0 1 1 11 16 3 2 5 12 14 198 45]\n", " [ 3 0 1 0 1 2 8 16 7 2 4 13 6 24 164]]\n", "Saving Confusion Matrix to outputdata/reduced_fulls_set_LogisticRegression_confusionmatrix.csv\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 0.66s.\n", "Scored accuracy on Test Data: 0.6868584961746887\n", "Confusion Matrix:\n", "[[275 21 8 12 17 6 17 3 4 0 9 5 9 2 1]\n", " [ 32 246 31 17 7 3 24 4 4 2 2 5 12 3 2]\n", " [ 26 46 234 25 3 18 11 0 0 0 22 3 3 1 0]\n", " [ 10 13 27 256 1 13 30 4 1 1 16 4 8 1 0]\n", " [ 62 44 8 4 240 2 16 6 2 0 8 0 1 2 0]\n", " [ 5 6 12 19 0 295 29 7 3 2 10 0 1 1 0]\n", " [ 2 0 0 4 3 9 334 14 4 2 11 2 4 6 1]\n", " [ 1 3 3 2 1 5 65 275 7 2 13 4 7 8 2]\n", " [ 6 0 1 1 1 8 30 5 301 23 1 3 4 8 5]\n", " [ 1 2 2 3 1 1 29 3 19 323 3 3 3 3 3]\n", " [ 18 14 18 26 3 21 34 19 3 1 213 9 9 3 2]\n", " [ 7 2 2 1 0 5 38 4 3 3 14 287 10 10 10]\n", " [ 17 2 0 5 1 3 36 9 4 2 14 9 278 11 3]\n", " [ 1 3 1 2 0 1 25 7 6 2 6 7 14 173 62]\n", " [ 4 0 1 2 0 1 27 9 8 1 2 4 9 21 162]]\n", "Saving Confusion Matrix to outputdata/reduced_fulls_set_PairwiseLearner_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training took 54.11s.\n", "Scored accuracy on Test Data: 0.664640113267631\n", "Confusion Matrix:\n", "[[248 27 14 8 22 8 18 4 5 0 17 2 10 4 2]\n", " [ 27 230 32 18 18 6 25 6 3 0 4 3 11 6 5]\n", " [ 18 35 239 23 5 16 14 1 3 1 29 3 3 2 0]\n", " [ 16 14 44 219 2 9 30 7 7 0 25 4 7 0 1]\n", " [ 43 33 10 6 239 2 18 3 9 0 15 1 9 6 1]\n", " [ 7 5 17 11 0 299 26 4 6 1 7 0 6 1 0]\n", " [ 3 3 4 1 2 10 298 24 5 2 21 9 6 6 2]\n", " [ 3 1 5 2 2 7 72 261 9 3 10 3 6 11 3]\n", " [ 3 2 3 2 0 5 24 7 309 15 4 5 4 11 3]\n", " [ 1 1 2 0 0 2 23 10 21 320 3 3 6 3 4]\n", " [ 20 10 29 12 2 14 41 12 7 0 214 12 16 3 1]\n", " [ 6 3 1 0 2 6 28 5 2 5 12 293 15 11 7]\n", " [ 12 2 3 1 3 5 31 12 5 0 19 17 263 14 7]\n", " [ 2 2 0 0 3 3 22 10 5 2 2 10 18 188 43]\n", " [ 6 1 2 0 0 2 21 6 13 1 3 15 11 24 146]]\n", "Saving Confusion Matrix to outputdata/reduced_fulls_set_MyClassifierSVCnoFeatureWords_confusionmatrix.csv\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(simplyfied_categories, vectorizer_params = {'ngram_range':(1,1), 'stop_words':'english'},\n", " cross_validate=False, consider_confusion_matrix=True, run_name=\"reduced_fulls_set\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "computer_categories = ['comp.graphics',\n", " 'comp.os.ms-windows.misc',\n", " 'comp.sys.ibm.pc.hardware',\n", " 'comp.sys.mac.hardware',\n", " 'comp.windows.x']" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting raw data ...\n", "Training data shape: (2936, 47493)\n", "Test data shape: (1955, 47493)\n", "\n", "\n", "=== Test MultinomialNB ===\n", "Training classifier ...\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n", "Training took 0.01s.\n", "Scored accuracy on Test Data: 0.7196877539586548\n", "Confusion Matrix:\n", "[[290 18 21 24 36]\n", " [ 41 222 62 15 54]\n", " [ 11 31 302 37 11]\n", " [ 13 18 50 281 23]\n", " [ 46 12 14 7 316]]\n", "Saving Confusion Matrix to outputdata/computer_MultinomialNB_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierNN ===\n", "Training classifier ...\n", "MyClassifierNN(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 31.28s.\n", "Scored accuracy on Test Data: 0.6865860217239537\n", "Confusion Matrix:\n", "[[283 36 17 15 38]\n", " [ 47 256 43 26 22]\n", " [ 23 52 265 42 10]\n", " [ 25 37 43 272 8]\n", " [ 56 46 12 17 264]]\n", "Saving Confusion Matrix to outputdata/computer_MyClassifierNN_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVC ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=200, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 21.89s.\n", "Scored accuracy on Test Data: 0.6676180263297067\n", "Confusion Matrix:\n", "[[293 39 17 16 24]\n", " [ 62 255 33 19 25]\n", " [ 35 54 251 50 2]\n", " [ 47 27 47 261 3]\n", " [ 85 44 8 17 241]]\n", "Saving Confusion Matrix to outputdata/computer_MyClassifierSVC_confusionmatrix.csv\n", "\n", "\n", "=== Test SGDClassifier ===\n", "Training classifier ...\n", "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n", " n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n", " shuffle=True, tol=None, verbose=0, warm_start=False)\n", "Training took 0.02s.\n", "Scored accuracy on Test Data: 0.7050903426149213\n", "Confusion Matrix:\n", "[[295 26 14 19 35]\n", " [ 56 236 54 24 24]\n", " [ 24 34 275 48 11]\n", " [ 30 12 43 295 5]\n", " [ 50 38 16 13 278]]\n", "Saving Confusion Matrix to outputdata/computer_SGDClassifier_confusionmatrix.csv\n", "\n", "\n", "=== Test LogisticRegression ===\n", "Training classifier ...\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)\n", "Training took 0.33s.\n", "Scored accuracy on Test Data: 0.7246468489329698\n", "Confusion Matrix:\n", "[[310 24 10 19 26]\n", " [ 57 252 37 24 24]\n", " [ 23 39 273 51 6]\n", " [ 30 14 32 303 6]\n", " [ 68 33 7 9 278]]\n", "Saving Confusion Matrix to outputdata/computer_LogisticRegression_confusionmatrix.csv\n", "\n", "\n", "=== Test PairwiseLearner ===\n", "Training classifier ...\n", "PairwiseLearner(learnertype=)\n", "Training took 0.05s.\n", "Scored accuracy on Test Data: 0.7075213387019355\n", "Confusion Matrix:\n", "[[304 29 9 24 23]\n", " [ 53 268 38 17 18]\n", " [ 26 61 264 34 7]\n", " [ 21 35 34 293 2]\n", " [ 68 53 14 9 251]]\n", "Saving Confusion Matrix to outputdata/computer_PairwiseLearner_confusionmatrix.csv\n", "\n", "\n", "=== Test MyClassifierSVCnoFeatureWords ===\n", "Training classifier ...\n", "MyClassifierSVC(n_feature_words=0, pca_dims=200, scale=True, svd_dims=200)\n", "Training took 20.32s.\n", "Scored accuracy on Test Data: 0.6780743435380616\n", "Confusion Matrix:\n", "[[304 36 14 11 24]\n", " [ 72 259 26 23 14]\n", " [ 32 56 257 46 1]\n", " [ 46 30 39 269 1]\n", " [ 83 51 14 15 232]]\n", "Saving Confusion Matrix to outputdata/computer_MyClassifierSVCnoFeatureWords_confusionmatrix.csv\n", "\n", "\n" ] } ], "source": [ "build_and_test_everything(computer_categories, vectorizer_params = {'ngram_range':(1,1), 'stop_words':'english'},\n", " cross_validate=False, consider_confusion_matrix=True, run_name=\"computer\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "alt.atheism: islam atheists say just religion atheism think don people god\n", "comp.graphics: looking format 3d know program file files thanks image graphics\n", "comp.os.ms-windows.misc: card problem thanks driver drivers use files dos file windows\n", "comp.sys.ibm.pc.hardware: monitor disk thanks pc ide controller bus card scsi drive\n", "comp.sys.mac.hardware: know monitor does quadra simms thanks problem drive apple mac\n", "comp.windows.x: using windows x11r5 use application thanks widget server motif window\n", "misc.forsale: asking email sell price condition new shipping offer 00 sale\n", "rec.autos: don ford new good dealer just engine like cars car\n", "rec.motorcycles: don just helmet riding like motorcycle ride bikes dod bike\n", "rec.sport.baseball: braves players pitching hit runs games game baseball team year\n", "rec.sport.hockey: league year nhl games season players play hockey team game\n", "sci.crypt: people use escrow nsa keys government chip clipper encryption key\n", "sci.electronics: don thanks voltage used know does like circuit power use\n", "sci.med: skepticism cadre dsl banks chastity n3jxp pitt gordon geb msg\n", "sci.space: just lunar earth shuttle like moon launch orbit nasa space\n", "soc.religion.christian: believe faith christian christ bible people christians church jesus god\n", "talk.politics.guns: just law firearms government fbi don weapons people guns gun\n", "talk.politics.mideast: said arabs arab turkish people armenians armenian jews israeli israel\n", "talk.politics.misc: know state clinton president just think tax don government people\n", "talk.religion.misc: think don koresh objective christians bible people christian jesus god\n" ] } ], "source": [ "print_top_feature_words(all_categories)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " = Missmatch Examples for talk.religion.misc categorized as soc.religion.christian. = \n", "\n", "Missmatch Example 1:\n", "\"\"\"\n", "\n", "\tI'm not sure that you can distinguish between myth and legend so\n", "neatly, or at all. A myth is more than a single story. The thought \n", "structure and world-paradigm in which that story is interpreted is as\n", "important a part of the myth as the story itself. Thus, I can think of\n", "no story which is meant to be conveyed understandably from one person\n", "to another within a single culture which won't rest upon that underlying\n", "thought structure, and thus transmit some of that culture's mythical\n", "\"truths\" along with it. \n", "\"\"\"\n", "\n", "Missmatch Example 2:\n", "\"\"\"\n", "\n", "\n", " just a point, i suppose, if open mind means believing anything can be true\n", "or we can't for sure know what is definitely true, i'm happy to not be open\n", "minded. if, however, open mindedness means being respectful and tolerant\n", "towards other beliefs, respecting the rights and intelligence and wisdom\n", "of people of other beliefs and giving equal time to alternative ideas, i\n", "try my very best to be open minded. just a thot in passing.... :)\n", "\n", "\n", " not being married, i cannot say too much to you, but from my perspective\n", "having mutually exclusive faiths would be a big enough roadblock for me in\n", "considering marrying someone. making it much bigger than it is? i suppose\n", "that depends on how serious each of you is in your beliefs. lukewarm atheists\n", "and christians for whom religion is of nominal importance probly would feel\n", "the issue isn't very big. i suppose the more important your beliefs are to\n", "each of you, the more important the issue is.\n", "\"\"\"\n", "\n", "Missmatch Example 3:\n", "\"\"\"\n", "\n", "\n", "\n", "\n", "And maybe they do. But without somebody to set the time that doesn't\n", "do them any good.\n", "\n", "\n", "Humph. Deleted there was my list of non-religious reasons one might\n", "want a moment of silence for a dead classmate.\n", "\n", "Maybe everyone doesn't want to be silent for teachers to give their\n", "pompous non-religious speeches in assembly. I know I didn't. So?\n", "\n", "\n", "\n", "Please provide documentation that opposing only things that are\n", "actively religious (e.g. actual prayer, \"Amen\" after a moment of\n", "silence, mandatory classes in religion) and not things that have\n", "possible but uncertain religious implications (e.g. moments of\n", "silence, having the Bible on the shelves during reading period) is not\n", "a way to prevent a state religion.\n", "\n", "\"\"\"\n", "\n", "Missmatch Example 4:\n", "\"\"\"\n", "\n", "That's right. Everyone. Even infants who cannot speak as yet. Even\n", "a little child will rebelliously stick his finger in a light socket.\n", "Even a little child will not want his diaper changed. Even a little\n", "child will fight nap-time.\n", "\n", "So far as Jesus saying \"everyone\": \n", "\n", " A certain ruler asked Jesus, \"Good teacher, what must I do to\n", " inherit eternal life?\"\n", "\n", " \"Why do you call me good?\" Jesus answered. \"No one is good--\n", " except God alone.\"\n", "\n", "Ken, the book of Romans states that we are born sinners. We do\n", "not grow into being a sinner. We sin because we are sinners. The\n", "common mistake, even in Christian circles, is to think the reverse\n", "true. So for as surely as you grew to look like you parents,\n", "you not only inherited their appearance, but also their sin nature.\n", "It goes with being human.\n", "\"\"\"\n", "\n", "Missmatch Example 5:\n", "\"\"\"\n", "In <1993Apr24.214843.10940@midway.uchicago.edu> eeb1@quads.uchicago.edu\n", "\n", "\n", "I already responded to this on one dimension, but afterthoughts cause\n", "me to make another, independent reply. The problem with a \"moment of\n", "silence\" is that it is NOT an even-handed way of \"allowing\" for religion\n", "amongst students in the public schools. As I noted before, Muslims need\n", "more than a moment of silence in order to perform the prayers they are\n", "required by Muhammad to do. And (at least Orthodox) Jewish prayer also\n", "has requirements that are not addressed by this.\n", "\n", "There is, in fact, a highly selective BIAS towards Christian prayer in\n", "this \"moment of silence\" shit. And that is especially bizarre in that\n", "Christian prayer DOESN'T NEED this stuff -- a Christain may pray totally\n", "incognito AT ANY TIME (to some extent, this is true of Muslims and Jews\n", "as well -- what I intend in my first paragraph is that there *are* some\n", "characteristic forms of prayer in *these* religions which DO need special\n", "times and/or behavior, which cannot be undertaken without an observer \n", "being able taking note of it.)\n", "\n", "A Christian may pray, at ANY time -- silently and without any trace of\n", "his activity being evident to others. That may or may not be true of the\n", "other religious traditions amongst us: certainly, these tend to have SOME\n", "forms of prayer that WOULD evidence differences from American/Protestant\n", "\"mainstream\" religion.\n", "\n", "All that a \"moment of silence\" does is to allow THAT ONE tradition which\n", "doesn't NEED it, to have a \"special\" place set aside in the public schools.\n", "There is NOTHING in Christian prayer that requires public forms, or rugs,\n", "or phylacteries, or anything else at all visible to the outside world. A\n", "Christian student MAY (and probably does) pray at innumerable times during\n", "the day, without anyone else knowing it. [That may also be true of non-\n", "Christians -- I am not claimng otherwise]. In the \"moment of silence\" it\n", "would STILL be difficult for the Jews to gather and daven, for the Muslims\n", "to do their ablutions and find qiblah to Mecca and engage in the prescribed\n", "forms. But *of course* Christians can do *their* thing -- and therefore,\n", "the provision is nothing but a disguised attempt to encourage just that.\n", "\n", "Luckily, there *is* a strong Jewish presence in this country (and I, as\n", "a Christian, revere some of the Jewish teachers I had in public schools),\n", "and a growing Muslim presence as well. I can only hope that the political\n", "forces consequent on this will PREVENT the imposition of Christian forms\n", "on non-Christians.\n", "\n", "As far as I can see (as a Christian) there is NOTHING in this \"moment of\n", "silence\" campaign but an attempt to use PUBLIC social pressure to FORCE\n", "children to adhere to a pattern that is biased towards Christianity. And\n", "as a Christian, I *must* protest such coercion. For what it's worth, I\n", "suspect that the coercion is not really targeted at the non-Christians --\n", "it is yet another case of FAILURE amongst Christian parents in \"making\"\n", "their children prayerful, so that they want the public schools to teach\n", "what THEY cannot manage to teach, despite having all the opportunity in\n", "the world to do so.\n", "\n", "If you have taught your children to pray, they do NOT need a moment of\n", "silence in school. If you have NOT managed to teach them, the moment\n", "will only embarrass you. Give it up.\n", "\"\"\"\n", "\n", "Missmatch Example 6:\n", "\"\"\"\n", "Can a theist be truly objective? Can he be impartial\n", " when questioning the truth of his scriptures, or\n", " will he assume the superstition of his parents\n", " when questioning? \n", "\n", "I've often found it to be the case that the theist\n", " will stick to some kind of superstition when\n", " wondering about God and his scriptures. I've\n", " seen it in the Christian, the Jew, the Muslim,\n", " and the other theists alike. All assume that\n", " their mothers and fathers were right in the\n", " aspect that a god exists, and with that belief\n", " search for their god.\n", " \n", "Occasionally, the theist may switch religions or\n", " aspects of the same religion, but overall the\n", " majority keep to the belief that some \"Creator\"\n", " was behind the universe's existence. I've\n", " known Muslims who were once Christians and vice\n", " versa, I've known Christians who were once\n", " Jewish and vice versa, and I've even known\n", " Christians who become Hindu. Yet, throughout\n", " their transition from one faith to another,\n", " they've kept this belief in some form of higher\n", " \"being.\" Why?\n", " \n", "It usually all has to do with how the child is\n", " brought up. From the time he is born, the\n", " theist is brought up with the notion of the\n", " \"truth\" of some kind of scripture-- the Bible,\n", " the Torah, the Qur'an, & etc. He is told\n", " of this wondrous God who wrote (or inspired)\n", " the scripture, of the prophets talked about in\n", " the scripture, of the miracles performed, & etc.\n", " He is also told that to question this (as\n", " children are apt to do) is a sin, a crime\n", " against God, and to lose belief in the scrip-\n", " ture's truth is to damn one's soul to Hell.\n", " Thus, by the time he is able to read the\n", " scripture for himself, the belief in its \"truth\"\n", " is so ingrained in his mind it all seems a\n", " matter of course.\n", " \n", "But it doesn't stop there. Once the child is able\n", " to read for himself, there is an endeavor to\n", " inculcate the child the \"right\" readings of\n", " scripture, to concentrate more on the pleasant\n", " readings, to gloss over the worse ones, and to\n", " explain away the unexplainable with \"mystery.\"\n", " Circular arguments, \"self-evdent\" facts and\n", " \"truths,\" unreasoning belief, and fear of\n", " hell is the meat of religion the child must eat\n", " of every day. To doubt, of course, means wrath\n", " of some sort, and the child must learn to put\n", " away his brain when the matter concerns God.\n", " All of this has some considerable effect on the\n", " child, so that when he becomes an adult, the \n", " superstitions he's been taught are nearly\n", " impossible to remove.\n", " \n", "All of this leads me to ask whether the theist can\n", " truly be objective when questioning God, Hell,\n", " Heaven, the angels, souls, and all of the rest.\n", " Can he, for a moment, put aside this notion that\n", " God *does* exist and look at everything from\n", " a unbiased point of view? Obviously, most\n", " theists can somewhat, especially when presented\n", " with \"mythical gods\" (Homeric, Roman, Egyptian,\n", " & etc.). But can they put aside the assumption\n", " of God's existence and question it impartially?\n", " \n", "Stephen\n", "\n", " _/_/_/_/ _/_/_/_/ _/ _/ * Atheist\n", " _/ _/ _/ _/ _/ _/ _/ * Libertarian\n", " _/_/_/_/ _/_/_/_/ _/ _/ _/ * Pro-individuality\n", " _/ _/ _/ _/ _/ * Pro-responsibility\n", "_/_/_/_/ _/ _/ _/ _/ Jr. * and all that jazz...\n", "\n", "\n", "\n", "\"\"\"\n", "\n", "Missmatch Example 7:\n", "\"\"\"\n", "-*----\n", "\n", "Far from being \"tossed out,\" the gospels are taken, almost\n", "universally, as the primary source of information about Jesus.\n", "I am curious as to whom Mike Cobb is referring. Who \"tosses out\"\n", "the New Testament? Undoubtedly a few *naive* atheists do this,\n", "but the phrasing of the question above seems to suggest that Cobb\n", "ascribes this more broadly.\n", "\n", "Perhaps the question that gets more to the heart of the matter is\n", "why, except for some *naive* believers (who, unfortunately, far\n", "outnumber nonbelievers, both naive and critical), are the gospels\n", "*not* taken as \"gospel truth\" that faithfully records just what\n", "happened two thousand years ago? This has an easy answer, and\n", "the answer has *nothing* to do with miracles: no text is taken\n", "this way by a critical reader.\n", "\n", "There is a myth among some naive believers that one takes a text,\n", "measures it by some set of criteria, and then either confirms the\n", "text as \"historically valid\" or \"tosses out\" the text. I suspect\n", "this myth comes from the way history is presented in primary and\n", "secondary school, where certain texts are vested with authority,\n", "and from writers such as Josh McDowell who pretend to present\n", "historical arguments along these lines for their religious\n", "program. In fact, most texts used in primary and secondary\n", "school history classes ought to be tossed out, even the better\n", "such texts should not be treated as authoritatively as descibed\n", "above, and Josh McDowell would not know a historical argument if\n", "it bit him on the keister twice.\n", "\n", "Let me present the barest outlines of a different view of texts\n", "and their use in studying history. First, all texts are\n", "historically valid. ALL texts. Or to put this another way, I\n", "have never seen a notion of \"historical validity\" that makes any\n", "sense when applied to a text. Second, no text should be read as\n", "telling the \"gospel truth\" about historical events, in the way\n", "that many students are wont to read history texts in primary and\n", "secondary school. NO text. (This includes your favorite\n", "author's history of whatever.)\n", "\n", "Every text is a historical fact. Every text was written by some\n", "person (or some group of people) for some purpose. Hence, every\n", "text can serve as historical evidence. The question is: what can\n", "we learn from a text? Of what interesting things (if any) does\n", "the text provide evidence?\n", "\n", "The diaries of the followers of the Maharishi, formerly of\n", "Oregon, are historical evidence. The gospels are historical\n", "evidence. The letters of the officers who participated in the \n", "vampire inquests in Eastern Europe are historical evidence. The\n", "modern American history textbooks that whitewash \"great American\n", "figures\" are historical evidence. These are all historical\n", "evidence of various things. They are *not* much evidence at all\n", "that the Maharishi, formerly of Oregon, could levitate; that\n", "Jesus was resurrected; that vampires exist; or that \"great\n", "American figures\" are as squeaky clean as we learned in school.\n", "They are better evidence that some people \"saw\" the Maharishi,\n", "late of Oregon, levitate; that some of the early Christians\n", "thought Jesus was resurrected; that many people in Eastern Europe\n", "\"saw\" vampires return from the grave; and that we still have an\n", "educational system that largely prefers to spread myth rather\n", "than teach history.\n", "\n", "How does one draw causal connections and infer what a piece of\n", "historical evidence -- text or otherwise -- evinces? This is a\n", "very complex question that has no easily summarized answer.\n", "There are many books on the subject or various parts of the\n", "subject. I enjoy David Hackett Fischer's \"Historian's Fallacies\"\n", "as a good antidote to the uncritical way in which it is so easy\n", "to read texts present history. It's relatively cheap. It's easy\n", "to read. Give it a try.\n", "\"\"\"\n", "\n", "Missmatch Example 8:\n", "\"\"\"\n", "\n", "\n", "\n", "\n", "\n", "In other words, the right of might.\n", "\n", "\n", "\n", "In other words, the right of might.\n", "\n", "\n", "\n", "\n", "In other words, he can do it, he did it, and your in no position to\n", "argue about it.\n", " \n", "\n", "\n", "\n", " \n", "In other words, you better do what this God wants you to do, or else!\n", "\n", "\n", "\n", "\"\"\"\n", "\n", "Missmatch Example 9:\n", "\"\"\"\n", "\n", "Well, where is he? Another false Messiah shot down in flames.\n", "\n", "Matthew 24:4\n", " \"Watch out that no one deceives you. For many will come in my\n", " name, claiming, 'I am the Christ', and will deceive many.\"\n", "\n", "Matthew 24:23\n", " \"At that time if anyone says to you, 'Look, here is the Christ!'\n", " or 'There he is!' do not believe it. For false Christs and \n", " false prophets will appear and perform great signs and miracles\n", " to deceive even the elect - if that were possible. See, I have\n", " told you ahead of time.\"\n", "\n", "Do we listen? Sadly, not all of us do.\n", "\n", "Peace be with you, and condolences to the families of those lost at\n", "Waco.\n", "\n", "Malcolm Lee \n", "\"\"\"\n", "\n", "Missmatch Example 10:\n", "\"\"\"\n", "\n", "\n", "The remainder of my article deleted stated why. One would be an egotist to\n", "believe that someone CARED about what Bill R. thought he needed\n", "to say about God. Whether they did or not is irrelevant.\n", "\"\"\"\n", "\n" ] } ], "source": [ "print_mixed_up(all_categories, 'talk.religion.misc', 'soc.religion.christian', n=10)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting Raw Data...\n", "Training data shape: (2243, 19179)\n", "Test data shape: (1494, 19179)\n", "Fitting the Reducer...\n", "Reducing the Data...\n", "Reduced Training Data\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Reduced Test Data\n" ] }, { "data": { "image/png": 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j16kVdOevU+tIKSmyOas87yoVeNtx+DjHTubhViuqfUoJUz9axOHjOefVTh2dSwk97KNTqzhdbh59ex52p2+KJ0CLBvUID7ECkJaVX2UuvKpJvvx1C9PGXXfebL0UGLL8ZXKcgdvAFGUOZcnApwPWX22wYsUKzGYzvXv39jn36aefsmnTJt5+++1zGuO5554jNDSUxx9//Jz6CST6zF+nVvlq+VZ2Hj6Oqvk69SCLiWfuut77uW2z+n7blXEoI/u82HgpEUjHfzb9SSn91mmoLdxuNytWrGDt2rW1bcoFR3f+OrXKgnXJPrF+AEXAu1NuI6HVqd2arZvUp2ubpn77MSoKCa0as373ERb+sYtjJy8PZcaLgUBJOhcXF3PzzTfTuXNnOnXqxJw5cwCIjY3lySefJD4+nh49enilI1JSUhg4cCAJCQkMGjSIo0ePAjBhwgQmTZrE1VdfzR133MHMmTN54403SExM9Eo/lCc9PZ0hQ4bQpk0bnnzySe/x2bNnEx8fT6dOnXjqqae8x5csWULXrl3p3LkzgwYN8unvgw8+4MYbb8Rms3Hw4EGGDBnCVVddRb9+/dizZw+FhYXExcXhcnnehgsKCip8DhQBCfsIIYYAbwIG4EMp5fRK5/8KTMSzl/0k8Ccp5ZFAjK1zcePw4/gBTEYj9SN8N0S99fBwxv97NnuOndolKwCL2cjiDbuZu3o7SFA1jRt7tGfa2Ov8porqXFgCIem8ZMkSmjRpwqJFiwCP1k8ZERER7Nixg88//5y//OUvLFy4kMmTJzN+/HjGjx/Pxx9/zJQpU5g/fz4AqamprF27FoPBcNqQTFJSElu3bsVisdCuXTsmT56MwWDgqaeeYvPmzURGRnL99dczf/58+vTpw/3338+qVauIi4sjJ6fiOtTbb7/N0qVLmT9/PhaLhQceeICZM2fSpk0b/vjjDx5++GGWL1/OgAEDWLRoEcOHD+frr79m5MiRmEymQP6VnPvMXwhhAN4BbgQ6AGOEEJXL5mwFukkpE4C5wL/PdVydi58V2w6Smec/bNAwMpTGUWE+x01GA7OmjuWx266hUWQYoUFmBiS2pn5ECDkFNkrsLkocLhwulSUbdrN4w+7zfRs6NaBM0nn9+vVeSefExEQ+++wzjhw54lfSubKqZnx8PEuXLuWpp55i9erVREREeM+NGTPG++e6desAWLduHWPHjgXg7rvvZs2aNd72o0aNwmComVLsoEGDiIiIwGq10qFDB44cOcLGjRsZMGAAMTExGI1Gxo0bx6pVq1i/fj3XXHMNcXFxAERFRXn7+fzzz/npp5+YO3cuFouFoqIi1q5dy6hRo0hMTOTBBx8kIyMDgIkTJ/LJJ58A8Mknn3DvvffW/MuuIYGY+fcADkgpDwEIIb4GbgW8alxSyt/KtV8P3BWAcXUuYtyqxvOf/+I3hm82Gnhl4s1+UyCXbdnHZ79sIqfQRq8OLXng5p643Cqj/jkLrdJisN2l8snPGxjaUy/hV9sEQtK5bdu2bNmyhcWLFzNt2jQGDRrEM888A1SUdK5J6myZPTXBYjklO2IwGHzURmtKfHw8SUlJpKamEhcXh6Zp1KtXj6SkJJ+2ffr0ISUlhRUrVqCqKp06dTqrMasjEDH/psCxcp9TS49VxX3ATwEYV+ci5lBGNi7Vt0YvQJPocNo1b+Bz/MOf/uCZz34m+cgJMnIKWLAumdEvfUFGdgFS+l9EPHIiD5fb/zg6F55zkXROT08nODiYu+66iyeeeIItW7Z4z5XF/+fMmUOvXr0ATyWvr7/+GoAvv/ySfv36+bWpvOxzTenRowcrV64kKysLVVWZPXs2/fv3p2fPnqxatYrDhw8DVAj7dOnShffee49hw4aRnp5OeHg4cXFxfPvtt4Dnwbht2zZv+3vuuYexY8eel1k/XOBUTyHEXUA3oH8V5x8AHgBo0aLFBbRM50ITYjVXmbkTUZraWZ5iu5OPftpQYY1A1STFNid//3QJTrd/528wKLw5bzVb9qdhc7gY1PUKxg/uRliw7xiXA1Hm0ICnep4J5SWdHQ6PPMeLL75I27ZtvZLONpuNoKAgli1bRkFBARMnTmTx4sXs2LGDJ554AkVRMJlMvPvuu95+c3NzSUhIwGKxeN8qZsyYwb333surr75KTEyMN4xSmVtuuYXbb7+dH374gRkzZpCbm8umTZt44YUXqryPxo0bM336dK699lqklNx8883ceuutALz//vuMHDkSTdNo0KABS5cu9V7Xt29fXnvtNW6++WaWLl3Kl19+yUMPPcSLL76Iy+Vi9OjRdO7cGYBx48Yxbdo0b0gr0JyzpLMQohfwnJTyhtLP/wcgpfxXpXbXATOA/lJK/zX7yqFLOl/6jH35S/alnUQr9xAIMpt45u7B3NCtXYW22w9l8MiM7ym2V70ZzB9GRUEo4Cp9OBgNCmFBFu4d0p2BiVfQJDriND1c3FwOks6xsbFs2rSJ+vXr17YpAWXu3Ln88MMPzJo1q8o2tS3pvBFoI4SIE0KYgdHAgkrGdAHeA4bVxPHrXB78Z9ItNI2OINhiIsRqxmw0MLJvJ66/qq1P2xKHE2cVmUHV4dY0r+MHz1pDbpGNt+at4bbnP+PDny6PClo6FxeTJ09m6tSp/OMf/zhvY5xz2EdK6RZCPAr8jCfV82MpZbIQ4gVgk5RyAfAqEAp8W7oYc1RKOazKTnUuCxpHhTP/+QlsP5RBVkExCXGNialXMYyQlV/MlHfmcfh4jo+sQ3UIPLN8BBWcfxluVcOtwsc/baBvpziu9LPGoHNxkJKSUtsmBJwZM2ac9zECEvOXUi4GFlc69ky5n/U99zp+EULQuXWTKs//deYC9qdlVbuztzIGRTAw8Qp6to/ltW9/w0XVDw27y82i9bvO2vlLqYFjJdKxEpR6iKCRCKO+XqVT99F3+OrUWVJP5p2x4zcZFBJaNWH6xJsxGRVsztOHirYfzjgr+6R0I3P/hMx/DGxfQfH7yKyhaLYlZ9Wfjs6FRHf+OnWWghK736Lu1XFFk/q8PXkEmXlFvPTVrzW65qzVQO0LwbkVZEnpATdgh4KpSGmv7kodnVpHd/46dZYrmtQHziwbLaugmCCziaVb9lepAFqZEvuZaaZIKZHObciidwCb/0bOzWfUp47OhUZ3/jp1FrPJyJN3XIvVXPOlqcLS0o5Olxu1huqRbZrVPEVQSg2Z/zdk7j2gViFPJUuQxV/W+OFT4VItB2n7AWlbiNQCq8AJoGX2RjveNnD/ZfrKIFcmLy+P//3vf6dtFxrqWexfsWIFQ4cOPed7rUxsbCxZWVkAXvnmlJQUvvrqK2+bTZs2MWXKlICP7Y+JEyeya9eu0zc8T+jOX6dOc0uvjrz759u4rmsbGkWFYTiNSFv7Fg1Yuf0gwRZTlSGjsh6EAKvZyBOjBiC1ArTiT9HyHkMr+h9SrUIe2rEU7L+CrGLGX4ZzDTiWnebuKqKVfIvM7I/MfxZZ8A9kZm80e81CVzUfJOuC91dT538hKZNwruz8u3XrxltvvXVBbPjwww/p0KH2pEd0569T5+ncqgn/vn8oz999fbXtTEaFXUcymfbJEt5ZsBa3qmEyGlCEQABWk5FberbnmoRWNKsfQf+E1nz8+J10aWVAZt0Aha+DfREUvYvMuh7p2uszhiz5nipDPRWwI0u+qfE9SvcRKHgBcAAlIIsBO+Q9htQubnnqqVOncvDgQRITE3nssccYNGgQXbt2JT4+nh9++KHaazdu3EiXLl04ePBgheMrVqzgmmuu4eabb6Zdu3ZMmjTJWyegKqnl8pS9ZUydOpXVq1eTmJjIG2+8UeGto6ioiHvvvZf4+HgSEhL47rvvUFWVCRMm0KlTJ+Lj43njjTd8+n7uuecYP348/fr1o2XLlnz//fdeyekhQ4Z4pZkHDBjApk2bquzzwIEDXHfddXTu3JmuXbv6fAfnil7JS+eCYHe6eW/ROhasTcatagzo3JopI/oSHV4zga1jJ/OY8r/5VWT+SFpE5XO8IBSHy4ijXAhfIBnWqyPBVhNDul9Jp1jfQu9a7p9BywVvSqgDpANZMA0R/e0Z3+spsxw1b2r7EfCjQSQE2JdC8Kizt6OWmT59Ojt37iQpKQm3201JSQnh4eFkZWXRs2dPhg0b5leMbe3atUyePJkffvjBr9zLhg0b2LVrFy1btmTIkCF8//339O7d26/U8vDhw6u07bXXXmPhwoWA56FSxj//+U+vVDR4JCSSkpJIS0tj586dgOetxh8HDx7kt99+Y9euXfTq1YvvvvuOf//734wYMcIr1VxGVX2OGzeOqVOnMmLECOx2e8CL4Ogzf53zjpSSyW/PY/byreQW2Si0OVi8cQ93Tf/KW5z9dLw1bzVOl3+BNrNBpUV0rt+1YbemkZaVx+gBiX4dPwDOleBvL4BrB1I6vfcg7T+Ddpya/doEIYJurUG7Muz4df5Sg0soc0hKydNPP01CQgLXXXcdaWlpnDhxwqfd7t27eeCBB/jxxx+r1Pnq0aMHrVq1wmAwMGbMGNasWVOl1PLZsGzZMh555BHv58jISFq1asWhQ4eYPHkyS5YsITw83O+1N954IyaTifj4eFRVZciQIYBH2bPypjR/fRYWFpKWlsaIESMAsFqtBAcHn9V9VIXu/HXOOztTjrPryAmc5dQ1VVWjoMTBzxt9Qyv+2LDnWJV5P25NYd2BljhV//rsG/elctsLn/G/H35n0R+7mbMiiZQK6Z3mKnqWSOmZkcqCacj8p8C9m4oPCkvp9WagtNiGCAZzIgTVfBO7sFwHogqxOcuAGvdT1/nyyy85efIkmzdvJikpiYYNG2K3+z7cGjdujNVqZevWrVX2VfltoSZSzudKZGQk27ZtY8CAAcycOZOJEyf6bVcmA10mQldmm6IoPmqlNe0z0OjOX+e8szf1pN/MF5vDxc6U46e9Pr/YXs0bgsQgJKpUOLWU64vLrfHhkg289OUy/vv9Ksa8/AWvfL3cY1fwKPz/Kihg/xHp2ge2H8rl85dhguBRiAarETE/Q8hDEDQOEfEGIvIThDiDykumzmAdBiKo9D4UwAqhDyKMzWveTx2kvGRyfn4+DRo0wGQy8dtvv3HkiP+MqXr16rFo0SL+7//+r0IopjwbNmzg8OHDaJrGnDlz6Nu3b5VSyzWxrTKDBw/mnXfe8X7Ozc0lKysLTdO47bbbePHFFyvISp8t/voMCwujWbNm3spjDoeDkpLK//7ODT3mr3PeaV4/wm8pRYvJSFyjKD9XVOSFL35BrUbXx6XVrCITeOQcTAaVfm1TMLm2k7THRuc290Hxh35aq1D8IdJ6HeBPTdQFWBBKJBCJCHu0xnZURggB4S+AdSjSvhgwIoJvRZgSzrpPvyj1A5vxo5w+TTY6Opo+ffrQqVMnunfvzp49e4iPj6dbt25ceeWVVV7XsGFDFi5cyI033sjHH3+MwWBg5syZfPih5++qe/fuPProoxw4cIBrr72WESNGoChKlVLL/khISMBgMNC5c2cmTJhAly5dvOemTZvGI488QqdOnTAYDDz77LO0bt2ae++91xt//9e/POLFM2fOBGDSpEmn/84qkZaW5rfPWbNm8eCDD/LMM89gMpn49ttvadWq1Rn3XxXnLOl8vtAlnS8dNE0y8vlPScvK9y7YCiA0yMKCf/7Jr35/GQ6Xm36PvXNGom7VEWqx892jX2MxuTEpKggjlqDO4NqOJ9OmEko0GFqAq4rwg9IcEbMIUVXIppa5VCWdV6xYUWGh9nKltiWddXSqRVEEH/3tDnp3iMVoUDAoCh1aNuTjx++o1vEDnipcAZyf/PWG36kXbCPU4sJi0rAYnR7HLvy9BCtg7gnqST/nStEykIWvB85AHZ0LhB720bkgRIeH8OYjw3G43KiqRrC1qkXWioRYzYQFm8ktOreMl4bhhVhNLvq3O0zlvV92l2RnWjjh1iDaNMxGCAkYQQQhQh9DFrwAztQqenaD7TsIf/qc7NM5MwYMGMCAAQNq24yLGt3561xQLCajNymmJqzYdpASx5lp75SnZXQur4z6hWZR+UgpKLBZMDs1gsyezKPF29vwr0XXoAiJpinEhBXz5rhlNG/YCoJYfooWAAAgAElEQVTHgaE5IvRhZM4f+A0LwRnl89cGUsoLkgmjc2E515C9HvbRqdPMWZGEo4r8/tNhMqh8MGE+rWJysJpUgsxuGkYUY1A8vzR7j0fz0o/9sTnNFDss2FwmjuWE8/Dn16M5t3jUObOHg/EKiHgD/08tBcyn17epLaxWK9nZ2efsKHTqFlJKsrOzsVrPfq1Jn/nr1GmKbGdWs7c817RNwWJSUSpNcaQUON2Cbzd2wlVpb4BEId9mZfuxaBJbHAf3AWTBSyj1XkEzzoOc0SBdeN4CrCAsiPBpZ23j+aZZs2akpqZy8mQ16xY6FyVWq5VmzZqd9fW689ep01zXtQ0HM7JxnEX93hCLg0K7iRBLxbCRxaSy8XAT9mZEo0nfl18hJHklZTMqF9gXIeV0hBKONPcH528gg0BpACIKWfwJhIxHGGPP4g7PLyaTibi4uNo2Q6cOood9dOo0d/TvTLP6EWck61zGj0ntGTHjLiZ8OIL0vDDvcYfbwvebO7H3eAwGxTek5FINdGxaXnJARWq5yOwR4FhSutnLBtoRULeCbQ4y61akc+NZ3KGOTu2gO3+dOk2w1czLf7qJqLBghABFCCJDg6rZy3sKicClGtiV3oD7Ph7OtmMNeW7+tTz17Y24DIOQUiHY7MJiPPVmYDW5mNBnK/WCyxZxS2P6tq9BK8Kv/g5uwIbMr7vhHx2dyuhhH506jc3pYtKbc8krtiElSCS5Racklc0GF8EWF0ZFI7soGOlnPqNJhdziIB789FbcWpl0QgoCGNT+AM2jCli5txVhVgd39NhJnzZHOSUeKj07Yu0/U2W2TxnqMTR3OgIXGBojRM3SWXV0agPd+evUSaSU7D56giUb92J3uvCXrBJsdvDNw3OICHIQZHbz8sJ+zN/SHk36yj24/UhASGD1/jh+mPIl4/tuq3DO4TJyMDOKTs0yS8XcDHgeGtXtNFYh63pk6YYxGfIoSuiFEenS0TlTAhL2EUIMEULsFUIcEEJM9XP+GiHEFiGEWwhxeyDG1Akch4/nkHQwDds55NMHksy8Im5/4XPuf2Mu36zchs3pf7G3a2wG0SE2gsye80/etIZ+bY/gf0uw/1TH7KJgft5xBXangTK59BKnkRV74pj0+S3879cepS1Vqnf8ZcJyTs+agCyBohlI24LT37COTi1wzjN/IYQBeAcYDKQCG4UQC6SU5YtTHgUmAI+f63g6geNkXhFT3pnPkRO5GAwKqqbx55H9uLN/4gW1w+Z08eHiP1i4fhealEhNkl9ir6Jwyylu6Lgfk/GUQzYqkumjljLm3TtIya7HKZVPiX/FT8mQ+H0M7ngQIaDEaWLrkcbM3dyR3/e3BARfrO/M7d2TaRBefJq7kPg+YGzIoncRZyDtrKNzoQhE2KcHcEBKeQhACPE1cCvgdf5SypTSc4EtRaNzTkx5Zz7707LQpPQIVAJvfr+aKxrX56q2Z58/XBV/7D7C69+t4lBGNgZFwWo2Eh/XiBM5RRzNyquyWEtVdGya6XNM1QSx9XNLnX/1Dw+TQWNszx0EWzxvDiajSp82R+nX7igZeaG8/evVrN4Xy5YjTRgSv7+ankTVY2l6fr1O3SQQzr8pcKzc51Tg6rPpSAjxAPAAUGX1Hp3AcCgjmyOZuR7HXw67082Xy7cE3Plv3p/KX95d4M3XVzUVp1vl92T/eu41Yf6W9vRolcp3mzuSXRhC37Yp7M6oz5r9Lag40/cfBooIKuE/P/Xm6tap3NFjpyfDp/SyxvWKmHbLSt5fWUSPVsf8XF+xryoxtj6zm9LRuUDUqQVfKeX7wPvgkXSuZXMuaXKLbBgrb30tJbvgdCGOM+ft+b+f1Uat6pi1NpHZfySU7tIV7MmoX1rNq3KIx39iaFZRKFlFYSSnN+S7zR356sFviQ49lUkUZHYzvk8SEUHnoN3j2oV0rEdYep59Hzo654FALPimAeVLDTUrPaZTh2nfvAEuPxr5FpOBfvGBKxhRxuHj2QHvU6LgUo2UOXenWt1cRlb6T3ivc2sG8oqtfLK6q89VoRYnmYU1KzLvHwey4PlzuF5H5/wQCOe/EWgjhIgTnsTm0YCe4lDHCbaamTy8T4Wds2ajgaiwEO7s3zng4zWPqRfwPv2hCMnQznv4YMI8Prr3e4Z3TS7dxVt+9u/7JqBKA2v2t/Q5npEfxroD51hGUT2MvISKsOtcGpxz2EdK6RZCPAr8jCcZ+mMpZbIQ4gVgk5RygRCiOzAPiARuEUI8L6XseK5j65wbYwd25Yom9flq+RayC0q4JqEVowckEhYc+KpUDw/rw99mLsAe4NBPRSTTb/+FnlccI7g0/bNto2wGtT/E5C+HUl2NX4DGEQWoGigChACb08h7v3WjX7uj52hXueLuOjp1hIDE/KWUi4HFlY49U+7njXjCQTp1jB5XtqDHled/cb1Xh5Zcm9ianzbuPW9jdGySWcHxgydu37n5cbrFprEpxfefYLjVzq1ddjM0cQ9xMXmUlRqWEj5Y1YVV++J4+pZVNRi9LM9fo+ICsNVT5F1UXWdYSjuy+CuwLwRhRQSPBevNuga/znmlTi346ly6/JZ0gN+2HfQ5Xj88mLxie0Bq9F4Vm45J8e3HanbTNTa9nPP3xPwbRRQy6/65hJidmE2+1z06aCMjuu6p8DCpGg1EBJivB8cCEGZPkRfrQETYU1VeJaULmT0W3AcAT2hI5ieDcz0i4sUajKujc3bozl/ngjBr2WbsfnbqZhWUVPhsNCiEBllQBOQU2nza+8fjzHOKg3CpSoWNXwBOtwFVVSibkYvSP/92wxoiguw+ev/gCfsIoFlkIVKCqoFBgTy3mWDFhVmR+EzMZQGYOiAiHgd3ChiaIQwxFZtIibTNheIPQMsFQzNwH6TM8Xuwge0HZMjEOikTrXNpoKt66lwQcgpLTt8Ij8Od/fQ42jSLOW3byizf3QrVjz6/xahyf/+NLH7sczo3P45EQSK4unWqX8dfwZ7S+H9Z3d+Xj3XlldSu2PxoBYEE11qEEokwd/Fx/ACy6C0oeBHUFJD54E6mouMvQwHnptPdso7OWaM7f50LQu+OsRgrV073g0Ty6jcr2LKvqoLp/vBMwUucZh6ZdQuZBcGUOIweFVDpcd5GAzQIL+Hde34gJqwI8Ii31XiE0oeASxpIc4Yi/SweSwkYqk6TlVoRFH8E1OCNRiigRNfYPh2dM0V3/joXhD/d0IPwYCsmY/X/5NyqZOX2g373INSEXekNuPmNe/hpRxsAn9CMySB5c+wirmycyfJdsZxGPggpwa2e6qRtUD7biuuT6QrCpVXsXGJAhNxVdWfqURA1eeAIEFaw9K1BWx2ds0N3/joXhPoRIXwz7W7uGnQV7Vs0oHu7ZpiN/jNgqhN0M1Qbp5Gl/xe0a5zlG5PH8zBo0zCHWfd/x/Cr9vjM3ytLR3tCPtJ7vE1QHlah8vCB/mwuaoBLU3BqCocLw3ng05HsPFrNQ8vQCGRVNYmNIEKAIDA0R0TNQgg9PVTn/KE7f50LgtPlptjh5L4hPfjy/8bx3l9G8fex12E1G1FqmNJoNRl4Yfz11WTrnzpzIj/Ubw0AqBjHrzy0EP4fAGXPo0H1Urkx6gjFqomnD/fkluSbeC6lOxEmB43Cc5j+9fIK17o1lVWZu/n80CrWZJ1AtVwHWCrfGUTO8jj8+t8h6i9FGK+o5pvQ0Tl39GwfnfOKlJLPlm7ig8V/AKBpGrf26sTjdwzgll4duK5rG8b+60uOnMg9bV8vTBjCdV3bknJ0Ph/8aqG6TVtfrO3MgCtTMIjASESVLVcoAp5qvpXxMXtIc4XQxFJCY7NnMXvasBXc8359NE2iKIJcZxH3rX+XHEcxDs2NRTESbWnGBx2GUc+9AJCgRCHCn0VYrgqInTo6NUWf+eucVxb9sZv3F63H5nBhc7hwuFQWrEvmrXmrAQiymLhrUNfTFmi3mo0UlHgE1h68ZgHNIguoTk1zR1oj3l52NQ63gTNZPqiqbdnbQInTiNOtEK64uCosy+v4AYyKxu099qAoAulM4tWtj3O8JJsS1YkqNUpUJxm2fN5IS0A03IxosAoRsxJhHVRzA8uRUpTJqszdpJYEXjdJ59JHn/nrnFc++mmDT36/3eVm7urtTB7RF5PBwK29O7F6x2E27j2K3eX2G65xulQOpXucnJSC0Vdv57Ul1S2ICmat68Ki7e24r98mhnfdg8V0ql6AWxUIITEopzKCpIQCm5Uwq53KyxFCQGZBMIu2taXEaeK+flt8RjQaJAmxVqSajpYzgZV5N+CuNL9yS5VfMrbRxBrJwAYdKd6cSmFuMQnXtCeyYc30j2xuJ49vncX23KMYFQW3ptIrph0vdb4Tk1LzX2m76mJZxnZ25B2lRUh9bm56FfXMwae9TkrJppxD/JKxDQMKQ5omkhgZW+NxdeoGuvPXOa9kVSEP7VY17A4XpmADRoPCGw8NY2fKcX7auIfvVm33yfbRpOSr37ay88hxbuhwDe/8GlnNqKcqd+UUB/Pqkn7Y3SZG99iBU1UwKJK03HDmb76SrUebcE27FIZ02k/L+vmk5YbRpqETo8H3FaDAZmH1vlj+d88CzH7OSwltW8QjS2aDdPlNBy2z7pODv/HJ3uWYDjqIfDMHmeli7NMjGTft9FVOX9+zkG25KTg1FUepGetO7uWDA8t5uO31p70eIM9ZzIR1/yPXWYxNdWJRTHx4cDnvX/0AbcIaV3vtv5Ln83NGEjbVhUCwOH0rd7bsxSPthtRobJ26ge78dc4rHVo2ZONe32IokaFBhAadWvgUQhAf15j4uMaYDAZm/7bVr+TD9kMZbD/UkKpLM+LnuGDGsl58sbYzHZqc5GRhCPtORGEySFyqgX0nYvhiXSJ/6reZ/u1SSM8Lp0V0Pgbl1CuIzWlk7qZOJKc1pNBuJTK4xCdmKgQI2xdg6owQLvqEZ7AmvzFqhZaldisCzAJXGytZf48h9MEjfDV9Hh16t6PLwPgqv09NavyUnoRTq1j1zKG5mXdsQ5XO/3BRJrvyU4mxhtMtqhXv7ltKpj0ft9RKr3fh0OD57XP5os/kKsfflZ/KkvQk7Jqr9G4kds3F10fWMrRZN1qG1K/yWp26he78dc4rfx7Rj4mvf4OjXDjHajLy+KgBVQqXPXbbNazffYT9aVnV9Hzmome5JcH8fqAlQqgIKC0C48HuMvHRqm7c0T0Zo6JysjCYUIsTRUgURbJqX0u+39wBTQqO5UQQE+Z/x7LEhVsrwYiVx5ttZXdJJEWqiRLNhAG10oMAMAq0aANqByuOXXZemv4Bn1/zKsFGz4PR5nby7dF1LD2+g2CDmZEtrsal+S93aVd900jdmsoz279hdeZuFKEgENQzB1Pktnsdf3kOFWVS6LIRZgryO8bqzN04NF+ZDg3J7yf30DJE35twsaA7f53zSoeWDfn0idG8++Nadh/NpFlMBA/c1PO0SqLNYuqdxvn7QZOeGbV3IViU/l8DBEFmF25NwWRQKXZUTrcEg1DZk1GfbnHpfPpLF47lepz8zrSGpGRF0rReAVc2yWT/8Sg2pzTirl47CKok+iZQ2Z53glirJNLo5Nv2S/gtrykHbBGsyG9KqjPMv+n1jRiBnOx8Xtz5PS8njsGhurhv/bscK8n2Otw9BWnUMweT66wYThMIukbGkWnPJ8Ro9Swwux2sOJ7Mmsw9FRy2w+4J1/hHYhBV54FYFTMGoeCWFR9ABhSsir4v4WJCd/465522zWJ446Fbz+ia669qy5rVOxHHCzGeKMDdtB7O2CjfxPwy3CrWrWnIIBOtBiu43AZSsj3rAlfFpvP3oSvIL7GSW2xh3taOrNgTh6w0Cy8pMbL00xCueg7u6ZvE8LfGoUkFRWi8MHwZgzocwqUqKEJyPD8UxY+CqFsTbCqM5onDvRgYkcqfGu2ia+hJ4kOyiTHZeDcjHrus9GtnFBj32ZEWgeOaUFZm7qLIZWfFiWTSbLkVHLdNdeFSVawGE25Nwy1VzIoRBcGewnRGrvpP6ZuBxCSMqGiolWb4qtRQEJgVI85yfRtQ6BoV533r8Pv30iSBDw/+irvSorwEBjbqVOV1OnUP3fnr1DlmL9/K69+uQA0yQ1w0zrhogv84gulAFsXXtT2VmuPSwKiAW8OYXUzwtjRA0KiHlecm/YFmMmJQNK8kc5PIIlbualFasavSQ0TVMOTaWP+zhdXdIuh1YwFmgxu728wd3XdybfvDWEyqN2OoeXQ+WYXBRAQ7vP1LCYWaiR9z4ijRjPyc14Ll+c14vdVqOgXncEt0Cl9ntSXLJXDJ0pCTXcO0ugiRp6LGmnENDMMqFPJdJazN2ofNXygHjdub9sSgKOwvPE4jazg/Z2z3eRtwyqqlqBUEIUYLznKZWOHmIJ6LH4WUkkNFJwBBXEgMabZczAYjDa0RNA6K5OmOI3g5eV7pG4JAQ+OlzqOpZz6XcpeBJctRyGeHVrLu5D4izSGMi+vHgIYdatusOoXu/HVqnWN701j+1WrsJU5aX9uRN3/6HRU8jr2UkqtboBScUr8078vEeLwQGWTClFmEMS0fT8BHsvMjOzsSQ+gxqLCCaqetWGHl6kaldX9LkRJUiTGriLBf9mIJkhzdZ6HPTZIp163jPz/3447uvuEdk0ESHWrj9Z97M/KqXTSuV8QxNZj/S+lFltuTLumSBlxS4Z9HezC3/RKCFZVP2/7KZyfasTyvGTaXgeJ1TgwbirBNboCrbxiYBBbFRKOgesRYwqv8zv7IPsCcfn8B4Omk2T4LwKfDjebzsChxOViQupnvj/1BoduOKjXcmopReLZCx4U2YHriWG5s2oW+Da5kXdY+FKHQq35bQqp5W7jQ5DqLuOv3tyhw2XBLjaMlWezdns69rQZwb+tra9u8OoPu/HVqlUUfLOV/f/kU1aWiqRrOVdtwJTbFW1KrDCHQIoK8YR/hllj3ZyH8ZASpBRov3BdLQq8iXvj8MGaLx/Ef3BXEsg3Nofxyg0slZO1hrHtPcsuELO5/Jh2pgcEAo3rsYkexlWCr//q7UgpW7GnF3K0dMXcugOYONN8cILJdVo45QmlhLSLC6GRK0x1MabqDbzJb8/q1XXBfe8rJG4XC4+2HYhAKNzXpwuwjv/sd+0hxJvsLM2gT1phjJdnIaja8gWemryExCsXvQi+AQ7p578BSn56cUgUJ+wsymLThA+b3f4IwUxDXNw58redAMDvldworLWjbVRcfH/yNUS17EWoMfJnSixF9h69OrZGfVcA7f/4Ep82J6laRUuJ2upH+dnmVF9UHXC0i8bfDV5T+p7oVktaE8dZTzVizKII3n2zGXyd2pKRJJZlkRWA6XkirDjYmTkvHYpVYg6V3yL8P2MbvtsY+Cp4AJwtDyCryzPLVRm4/jr+iYeVvq9BtZFZmO59m7cKbcEOTRADahDfCWEX5Rw24b91MVmXupmtk7Glzn/rEtOPGJol0j26NuRpl0eoeIRqSQpedDVkHTjNa7fJH1gG/GVFGxcCBwuO1YFHdRHf+OrXGpp+3Yay0ldackuOrrFZGueNamIWSq1pQFjr3d4WUgl++iebZR1ozT+tHzoiuFR4gBtxYTuRjLrIx+I4cTBbfXjQJO0uiyXNbsJWmhjrdCjankecX9geTxNCrAGEqu9a3j2iTnebmIoo1I3luMzPTOzB+73WcdPumU+4pSOfhDR/y9t4lZDkKaRJU9a5fu+bipZ3fMya2T5VtytiZf4y+MVfSNaqVT6bOmSClRpaj8KyvvxA0Cqrn92Ho1lTqW/xnW12O6GEfnVrDaDL4ZO8Y8u0Ebz6GvUdLtDIpTQlIjTLNBWN6PkHb0lGK7AhFAVWtMnXR2tBA1rBEmsWUcGeP1bSIymfzrhi+39KB0EjJa3f8yLJG9Yi90obBzyRbInBhYPSeG7il3mG6hJ3kqCOM7/a3I71xKIZO2VTMjBSYhYpLKliFG4OQvBy7Hrtm4JecprydkYhNM1a5+1eVGptyDrEp5xBfHV5DxGnkFmyqE6emEm0OI8tZtVPOdRbzzx3fMb5V/9MEiKpHQ9KpXvNz6OH8My62L+tO7vNuRAMwCgNXhjelWbBeIKcM3fnr1Brdb+yC9BOzr7cvi7/9+152FBSyZOkWsrILcUcFgxCYDmVhzCnBHRWMJbsYXCpVbfiyBGt0mWTlZFAm/xm9BJOiYjRKujRNY/TVO3jgvm6E3+nmoRfS0cpV/SqPUWhsKGxIsWZidnZbvs5p6zkRBkqYy3dQPPkvN9RLJT40i75hx6lncvB+Rge+Otm21OnXbIOaG41sZ1G1bVSpsf7kPlw1mM3bNRffHfvD7z6BmmA1mLimQXviQhsgpfRu0isL01W1ae9CkxDZkqkdb+W13QvRpIZbaiTUa8nLiWNq27Q6hfAbXz3TToQYArwJGIAPpZTTK523AJ8DVwHZwJ1SypTq+uzWrZvctEmvYXopYyu288nrC1jw8jwMBgUhPY5k7NMjGfv327hr+lfsT8s6JfOgaZTu1wLAfCiL0BUHEX6KvwhFMui2XLZ17M5rd/5Mo4iKzs7tgp/nRPP9B/V58q1jZGUYiYhyc0W8DWuQRNXAiYH3Mzow+6QnNu/v4VAZk1AxChWbZqKByUYDUwkH7RHYNBPVS1KcOQKPQ7ap/h9CVTG57Q3M3L+sRg8MAAOCVmENGdn8auJCY3hu+1yO2/NQEESaQ8lzFSOlJDEqlr9eOZS24dVrA/nDrakcKDxOiNFK8xDf2fm+gnTSbLm0DWtM0+CoGvd5pDiLCFMQ9a1VZ05dagghNkspu52u3TnP/IUQBuAdYDCQCmwUQiyQUu4q1+w+IFdKeYUQYjTwCnDnuY6tc3EipeST5+Ywe/p8TwhCVXHWjyA4oRlvvHofbTs0Z++xTA4fz6mo7yMEGE85T3fjCP++VIFRj2Zy39Tj/O3rDtQL9s3WMZrg6kH5vDutCTOmNmH/thCEkHQfWEi/W3KRrS18G9aeQ/YIghUXNqcRbAqElXv6VLwroCy90xM/ynQFk+kqH7YJ7MxYwhk7foAe0W1YkrGNA4XHaxQCSoyK490eE9mXn85d6972HteQZJcLNW3JOcxda2dwTYP2vNj5TqwGc43sWXE8mX/u/A5VSjSp0Swkmte63E2T4EgKXTb+vOlTDhQex6AouDSV/g3a83zCHRgV/4vhZRgVA63DGtbIhsuRQCz49gAOSCkPSSmdwNdA5e2ctwKflf48Fxgk6so7os4FQ0rJ3k0HeX3iu8yZPh9cKsKlIjRQjhdSkJrDh2u2AXA8pxBjTgnm/ScxZBb6XQTWQi042jXAUC7F3GTRGHhnHn96ypPVcX3HgyhVFHQpLjRgNEkefCYDRZFIKdjwazj/+UtL3hrZgGei/+Cp5lt4qukWettO0jYip+xOyv0pMePm6tDjWMTZ1R2+kAjgv3sWE2MJr0bi4RRWxcTdcf0A+OfO72o0xu+Ze5me/EON2h4qOsE/tn9DodtOierArrk4VHiCRzZ+hCY1Xtz5PXsL0rFrLordDpyam1WZe/gqZU2N+tepmkDE/JsC5WUbU4Grq2ojpXQLIfKBaKCCeIsQ4gHgAYAWLarXftG5uLAV2Xj6ppc5sPUw9hKHT1KMUDXMu0+wYut+CnKL+GTMW5j3pmNGgALuqBAKb+6AtFT8J+vo15Lrrj9O9uoSSooUbhiTw8j7T9XvHdzpAAdORBEXnYvJdGpQW7Fg/of10RC0aGvningb+7admqULAeHSxQ2Rx3CoCu9Fd+CEMwTv7F1KkNDaXsy9rXay1tEQh6x+JloXkMDm3EM1bm/XXHx7dD1xoQ04VJRZo2tUNJYd38HUjsOxGqrX+3l33y84tIpvLxqSXGcRm7IPsSZzj094ylFq0z2t+tf4PnR8qVMLvlLK94H3wRPzr2VzdALIB09+wd6NB3E5qglTaBKRnsedjSficqqlblaCCsasIoLXHKL42jaYjuZiyLehRoVAy1Due+AwTZ70n+miCGjbKIeiEiOqS0V1C0xmya/fRbJ4Tgz23i3YmplfKZYvaRLnoF59NzbVwKr8JqQ7Q6kQthECqcHBY/X556obaDX4iGfF6xLk95N7uXXlq2d8XbHbXq3z35B1gNWZe/yeEwhOOgqqDEvZ3L6yF6ejxO1g2fEdpJXkcGV4E/o1aH/a0NGlTCCcfxpQPverWekxf21ShRBGIALPwq/OZcKyL1ZV7/gBaTYQvmg3LuFHkV+C5VA2puMFCIcboUqkQWCJMmB+yP8O3PJYLG4emnkjMRkZHF4DmUVh2AY3w9W8Hu+utJC/b5+noRnMZpj0egbpjmDmZrVmzsk2+IvXCwNoYRolDiM7/2iBoXdBDb+NS58IUzBR5lBO2PLYnneUaEsYiZEtUcrlxb659ye0Kty7S6p0j25Ns+AoUopPVjinIOgV0/aM7DlSnMXE9TNxai5sqosgg5lG1np81HMSoabLc8dvIJz/RqCNECIOj5MfDYyt1GYBMB5YB9wOLJeBSDPSqZOkHzzOxiVJWEMs9Bneg9B6IbgcVYuMQWkejFPFHRWMIc+G321bmkQpcnrdsNAkzpOSd55rzpOvHyXI7K4mI0eQlN0STLFQSd7lUFYU1ocaYthlQ2tiouC6CB6iLWJ39fcp3SBzjR510OyaLW5eDhhQeLLDMP6zeyHzUzeW7lKW1DOH8L/uE2kS7FFbPVLJqZdnZPMeNLBGMK3TbUze9DEuTcUtVSyKkSCDmUfb3nBGNj23/RsKXDavDIZNdZJaks37B5bx1/ZDz/peL2bO2fmXxvAfBX7G8+L7sZQyWQjxArBJSrkA+AiYJYQ4AOTgeUDoXIJ88o/ZzP3PjwAoRgMzHv2IZ797nK6D4/+/vfMOj6pKG/jv3Ds1mYQUQhIghN6lhioiItixi+xaYC0ssta1rGW/3dUVu5T6vxYAACAASURBVLt2V2Xtrl0WBWxgV8ClSZXeQkhIID2ZTLnn+2MmIWFmkglJJu38nidPbjn33PdkMu899z1v4X+frUMGccsE/7zakJQPSMayvwDL3iNUX6etmaG/2nWG5MdFcTx+ehpnDviVoek5mIJYGsoNHUwS3IFPBmkzcJ8ai/vUo+6AdS2FSgPwCuRef5SuqX3NZapXTahOB3MUjw6/nBxnIR8fWIXL8ODC9+B3lru5fe2bvOWvFJZkjeFAeX5AH3bdwqnJg7l3w/uUuCuY02cq2eUF7CvNY0h8Ny5IGxNWreFKStxOthZlBeQ/cksvnx/8RSn/hiClXAIsOebYX6ptO4FLGuNeipbLxh+28OE/F+NyVpp3fL/vu/gxnl7+AL+u3E5FuYuKstD2Wmk3Uza+O+bsIqTbQPMaSF0gNQ3hDuGXbkj+u2Ygny9K5tNHP8Bhrul143HDFy/HocUWYBTEg7eaatclWnpZ2C74UgJekIcsGBsd4NZAl4ju5XVf3IYI9ah7YuRMNKHx6OaPcXoDF3J3leTwmx+eRBcaZs2EjoaXo5+XTTMzIr4HN6x6hQrDg0Ty8+EdDOjQhWcyrjo+G73w1YAORqmnbpNhW0Xl9lE0Gl+8/i2u8oqA40LTOLAjm1e3Pc3Mey/1RfYKcCfHUDquO6Vj0vEk+nLBW3YfwXBYKZgxgvKMNCp6JlI+vCsFlw7DkxQdoHSkgIoeieD2YlqbzbN3d8FZJjD8+sTlFBTlm3j/2U7EPvAruv0ImAzfIoLFQKQVo/esCN8F3wDv/2IxVsf63iI0iUipQOsXvKxje+Ofvy7m2hUvUOQJ/jD0SoOdJTlsKz7ot+X7Yp5NaESbrMxIH8//juzEabhrmGi2FB7gm0Obg/ZZFw6TLeRDw214KfUE/s+2B1qUt4+idePLyBl4XAAet5eYeAeX3HouianxfLf/IKbsYuyr9oGu4eydiCs1FvvmbMqHdcaIi8I5rEtVvn1RUoHhsCKLK5AeA81jYJg1pNVM2dh0LLsPY8ks5KvMBA7utXLh73Pp1MXFmm9jWDA/iaIjJnQg7vGteDQgxYIscOP6Ywoe3YGOwBtGyJPQwTSuCOkUvmhjs0TU4s0oqqoMtA/WF+yrV3svsmoB95ERl/N1ziZMQq8yFVVS7nXxwd4VdLbH0z+2c9XC8a6SHBZlrqHYU87ETgPo5Ujmw/0r2VGcwwlxaVyYNoYEqwObbq5RtawSk9Ap97paVD2CSKGUv6JBHNyVw8JnP2XflgPEJXfAGmWloqzmTMrj8TJy6pCq/RfnfYDt12yQ/gm328C+MQfDYcGwm4n7cD0VfZKo6NURrcKDbXM2poNFOAelkD9jONbdR9AKyvEmRuPqmQi6hnAdNQltWR3NvNmhq0qZDHCnm3H+JQUjzbdQm2zuQKFRTomnos4UDgDCFp46bz9q//gxkKw6sgtdaETr1pAvYesL9jH35/nEmO08MXImmwsP8Mjmj/EYXrwYfJa1DrfhRfPXGF59ZBfv7P2JV8fN5aSk/nyatZZjw/CSbLEkWhxNPcQWiVL+iuNm44+/ctcZ9+NxefC4vZj9AVhWu4UKpwuTSUcz6dz0/LU44nzKOD+ngMO/ZnFs0K0AtFIX0mpCGBLb1kPYttYMKtILy0EIKvofE7Lv9qIfqdvsUnlL1yQHzhuTEWUGIs+D7GgiqzyfOFuUT64g2iecvD6K48fujwcYldjLZ6IJsrzjkV48Xi9lXhdz//dvSt1OX6EZP5W1jg3/MZfhwWV4uHX16zyZMYtvcjZT6q05MTmxY78Wk5Au0ijlrzgupJQ8dtVzOEuPfpncFR6EBgPG9qP/6F444h106ZPKa395l8evfh7Da5DQOT7k2qqQgDO0S6g5sxCt1IWha0fz8hsGwmNg3ZEX8rqq/gEjSafi4nii/5SJvsflWwxMNFF+RwoFPSSYA5fBlFNy02ISGim2eD7Yu4LTU4cysdMAPjmwutZryjwuhNAgjOR0u0tzeWfP8qAFXj4+sIqL08fSw9HpuOVvrTRKVs+mQGX1bNkUHSlmeuq1eEN44NiirfTN6MX6bwMX6RqS29KwmSg5qRfu7vGAwHSwEMd3u9CLwvPakAJkgo444q3pSmoXFM/vDvEmhL/kYdU5NetvUjR8yyeV0cCGlEHt89Ux+W3+oUpSBrbXMWs++351dDRm9jyZOX2n1lvulkrEsnoq2ifO0oqQir/yfDDFDw3Lbak5PcR8ubWqkxA520IiJFBqBF7nBfPSIlyXJNDbkUyeq5hCVxnRJitu6cXl9YSMRq2SDQJsyjoC4bdBK4JT+Tc71jW0NsJV+kfxZQwNvLeBW9b+oGmrKOWvOC4WPLUEIUTwertNjIDai80Gu0YTSEOi6QKvK0j+f5dEO+TGppu5sNtoLuo2tsb529e8wbeHQof8moTOjPRxvL9vBbp/VuqVBncOPJ9/7/qazDKVzaQ5MQk96MPbqpmZlDyoGSRqfpTyVxwXP3y0slkUfyX1NR31Gd6DmMQYEjvH8807P1YLRPP3ZxPIwdHEmaM4s/PwgOt/1+sUVuTtCMhAiV+O8R37cn2/M5jZcxLL87YhEIxP6kuM2c4pKYO4edVrrCvYU6uM2jHmJkXjYNPNzOo5CYtm4oXtS3EbHiRg1U2c02UkJ8R1w5AG+RWl7CnNpUtUAim11E5uKyjlr6gVKSXOUicWmwW9WrF1k7n5syGGSvlwLGarmTteu570gb78g4W5Raz7euPRSGOLQCRbOevSScwZeBpRQXy+B3boyiPDL+PhTQs56MxH4gtMSnd05Kpekzk1ZTCa0OhgieKMzsNqXBtlsvLCmGt5YOMCFh4Ivo7VNyaVpzJmcc43Dx+HSUMRigSLg/O7ZtAjOonujk6M7djH5xIqvZyafAIWTed3y59jU2Em4HtDEMDYjn2YN2xG2AVpWiNqwVcRkhWLVvPMDf8mNzMPk9nEOb+fyrWPXIG7ws15cTND5umJFB6HBa3MjTBqlkOvNEdpuoZu1rjlhTlMveJo7neP28OCp5awZP4yPC4Pky49kRl3nk90bN35YqSUlHldWDVTvVMNuAwPM354goNlBTVSGqTa4njvpFuw6maWHtzA3b+8Xa9+2zIC0IWOV4YTglcTXWiYhIaUErNuwmMYDItP59ERl2PTLewpOcQVPz0b9G3OJHSiTRaKPU46mKO4vPsELutxUo2spC2VcBd8lfJXBGXTT1u5Y8q9AeaRYZMHk9qjE5/++6tmkuwoUhdIi8B9sgPP6Gi0Ix567ojmlU8eYtOPW6koq2DwhP7YHfZmlXNncQ6bCveTZI2lX2xnXtqxjGXZG9A1nXM6j+Dq3pNr5L2/ZdXr/JgXPM99MLpHd+RAmS9BWm11eXs7Uri5/9ksPfgL/w3xBtKSEMC8oTM4XFHCa7u+Jc8VWLNBQIB3Vm1YNBPndc2gW1RHnvh1cVhR3eDLOXRRtzHc1P+seoygeVDePooG8eb9HwQofoB1X21kV2JMM0gUBDOU/qMrRicz2DXwSrZNFdx83z84u3sGp8wYj9XefGH7Xmnw53Xv8EPuVl8Qm9CIMdt4YfRs/jTo2EqnUOAq5dOsteS7SsLqP1q38oe+p3FRt7GUeioo97r4aN8KXt/zfYBP+4DYLkxJGcxDmxZwqKKwMYbX5Egg31WKSdODKv7KNvWJonYZHhZmrkIT4aXzqMRpuPlg30qu7X0qUSYrBa5SlhxYy/6ywwyNT2dyymAsWutSp2rmrwjKb9PnkLs/uIeK2WbGHeTBECmi46IYf95oPq3YSOn0DmA75lW8zEunq7NITIrjmZUPEhPfPOH77+1dzjNbP8NZzaygIegf24VXx8+t0XZzYSZzf/43Humt08cdIEq3cN+QS1mctYZ1+XtIsDiY3m0cZ3UexufZ63lt17cccZWgC40KrxuX4UE7JoNma+CUToPYXJRJjrP5H1hRuoVXxs3FZXiY8/NLeAyDCsNXGKajNYZXx80lxhz+W2aJx8lnB9axsySHfrGpnJ46DLup4WsM4c78W74BS9EsdO3bOeQ5s7X5Zjj9x/Thw9yXueOVP5B0Za9AxQ8goTQVcvfl8db94RUdbwwqvG72luZR4k8T/NH+lTUUP/jy2OwoySbXebTql5SSv/zyHmXeirAUf+W97lr3H77J2Uy+q5SdJTk8uPm/nLz0XrYUHuDtCTfxx/7n4DI8/tTItDrFL4DOUfHku0obtU+rVntd4VB4pUGyrQN/Xf8+pZ6KqrWCcq+L7PIC5u9YFnZfB8qOcOG3j/PU1k/5cP9K/vHrYi76/vGIPuSU8lcE5aoHji3G5kNootlMKdYoK7e/8gd03bfQmpyYELyhLhBlBm6Xhy/f+JafFv6P8pKmzbf/xq7vOO2reVz50zOc8dUDzNv4ERUhgpY0RA0lf8hZSLazoF738yJxS2+AycNA8knmKu7f8BGfHFhVr8CphiKABLMvQ2p96O1IxhZEIUvgl/y9dI9OqrcsGgIthBy1WTsSLQ7Gd+wbYMKxaWYuTBuD0+tmf2lgKhG39LI0e0PY8j2waQGF7rKqyYHT6ya/ooR/bFkUdh8NRSl/RVD6j+rNKTNODDhuspiITYiwGUVAYmo89y64nW79u1Qdnp4+riohWBVeiXbIg7bP58ZZfLiEh2c+zSUp1/L9RyubRLzPstbx0s5llHtdlHtduAwPn2WtI8ZsxxLEIyjOEk1ne3zVvq75yhzWhgAsIjzvIpf08lXORlzeyEauSqDQXUrnqARMYaoWq2bmnC4jcYd449lUuJ9JyYMwhzn2Sgxk0EVgCbhCRPSmRSUyf+wcnsiYxRMjZ9LL4UsgGGu2M6vnydzU/8yqAL5gmMKU8e3dP/C/wzsDHtxeJD/mbg2rj8ZAKX9FUKSU/PrzjgAneiEEqb2Sg1/UFAi4d8EdvJ35AiOnDq1xakJSf2akn4hFM6E7gTIDkesh6m9ZVWJLKSkrKqeirIKHrniKvAONH2n7ys5vAmbYFYaHXcU5pNrjsft9xc2ajl23cN/Q6TUySXa0xtDTkezP/R+ISWikRXX0PyTCw6zpxJrDL3UYCoEv/034FwjuHHhe2JkyzZrO+KS+IV0oJb4HgBaB5EoHyo5w9Yp/4fS6yUjsxdsTbmLl6fNYeur/cVXvyVVxHAPjuga8VVg1E+eljarzHsuyN/Dc9i9Cnq/t4dLYKOWvCMq2VTvJP1QYMCF1OV388s2myAki4aErnsbtCpytCSG4ru9pLDz5dv7UZxppz5fS6eYcrIeDz6KlIfnm3Z8aXcTDoTxRBDyTcRV3DDyXaV1GcFXPU3j/pFsYFt89oO0Dw35DotVBlG7FounoQiAQWIWJ01KHMH/sHMz1UP4ew2iUEoW3DZiGSQtfTXSNSuDeDR/W6nJaHYfJRrItLmhgXSUbCvZVpWtuSgwkJR4nX+dsrDoW7CH29yGXkmSLJUq3YtVM2HQzQ+O7c3mPk+q8xys7vwk5FrPQOSN1WNBzTUHr8k1SRIySglI0LVhieygvDjODpv93Q+ds5cXlPHndS9z+8tyg5xOtMZw/aBzTPhvNuq82smT+Mn78788Biec8bg9lxY1v+x/UIY3ledsCjsea7CTZYjm7ywjO7jKi1j66RiWy8OTb+TF3G4echQzq0JVBcWk12pzdeTgf7v+5zkVhm2bmsu4T2FmSU//BVOOcziPpE5uKSdPrVL6VkbHZ5QX1UtTZzgL+uv497h50AX9a91bQNkXuyNVHdhkePslcHTTFRyUp9jgWTLyNFXnbyXYWMCC2S8BnFYq8iuATBYBu0R25of8Z9Zb5eFHKX1GDzG1ZHMkuIK1/Fzy1ZO0MByPVhDQL9H3uBj8Alr31HbMfuZwOHWNDttF1nZFTh5KQGs+KRasDlL/FZmHMWbUr4ePh+n6nsy5/D07v0bqzNs3MLQPOrldEqFkzMSl5YMjz1/U9jc2FB9hanAX4FjVTbHHM7jOVBftXsqkwk3hLNFf2mMi5XTNYc2Q3K/K2B3gchcOfB13AtK4ZlHoqgubBNwmNvjGpJNvisOgmutgTeHPP98c1Q/8pbxu3DZzGTf3O4umtn1bZ6psr19G6/D0crigm0Ro6nsWk6Uzo1L9q32V4KPO46GC212ryOiEuje8O/Rpg74/Wrbwx/vrjK1B/nCjlrwCgILeQu86cx+71e6vSNgwY149NP4YfaVodCXh7WdE2uUAT0MBUEBaLmXVfb+LkS8bV2bbH4G6cefVkPn/l66piM7ZoKydfMo5+o3o3SI5g9IlJ5ZVx1/HS9mVsKsykS1Q8V/WazKjEXo16H5tu4cUxs9lcmMn24my6RScyPL4HQggmpwRmphyZ2JO5fU/j2W1f4DbqTkldSdeoRM71268dZhsze07kjd3fV61r6AhMwqf8zu86io62WD7JXF2rvVoXAm8ILxuzZiK7vJDLekxgcsoglhxYS6G7jA/2rcBohjgki2ZizZHdTE0dUmdbl+Hhn1sW88mB1UgpibNEcduAaZySMjho++v6nsbPh3dS4XVXfR42zcxtA6dFVPFDA4O8hBAJwLtAd2APMF1KmR+k3WfAWOAHKeU54fStgrwiyw1j7/It8DYT6YPTyN2fR1lh8Ff8qFg7f373j4w6PTybqJSStcs28OUb32J4DU69bCKjzhjWLkv2lXicrDm8m08yV/NtbvAaC5VYhYm7B1/AmV2Omj2klCzL2cibu75jT2muL25ASkyajkBw35BLEELj3vXvB5RJBJ8XzW0DpvHU1iXsLDkUcN6imVg06U7iLDUXqE/+8m8BxVciQbRu5YFhv2FcUt862967/n2WZm+skR/Ippl5MmMWwxN6VB0rcTtZfGANO0qy6WiJYWdJDluKDpBqj+eqXqcwtmOfRpM/Uukd7gSWSSkfEkLc6d//U5B2jwJRwO8beD/FcXBofx7vPvxffvlmE8ndO3HBjWfRb1QvKspdFOUVY3dY2bpqZ7PK+NSP87A7bCx5aSlP/WE+hrdmQJLJYmL45OCzqWAIIRgxZQgjptQ9e2vrOEw2JiYPYGLyAF7e8TX/2vFlyLZW3UwnW4cax4QQTEk5AYfJxp/WvoXXn3W0cu3hrxveZ+HEO4KauKyamX+OnEm36I6kRydx2Y9PUeatqHoHselmzuuaEaD4AU5LHcqSA2vCXjxuLMy6qcZbm8vwsLkwE7tuoW9MatUEoshdzpfZGwLWYJyGm5d3fs3TfuV/oOwIs5Y/R4XXjdNwY9PMRJl80cKp1Vx+I01Dlf95wCT/9mvANwRR/lLKZUKIScceVxwfhmFQmFdMdKwdi632cPDsPYe4buQdlJc48bq97N2cyc9L1vhOCl+xdcNrNGuGTk0TCOFTMmfPnorX4+WF217HZDGB9KWTePDTezCZlZWyIRxyFvLG7u9qbVPkKefm1a/y5ok3kh7dsca5T7PWBp2J62j8UrCXZ0ZdxS2rX8PpdVUlW/u/wRfSzd9P56h4Xhwzm9vXvklWuc9AEKPbODU5+AP6pn5nsqUwk/1lhzGkgcsIDGqri+Hx3fFKgy2FBwDoHp1Eobs8ZH4jq2bmmYyrqkwwC/at5LEti/BKAyEECRYHz4y6ih6OTuQ6i9BCOEweKDtStf3w5oUUucurZHcablwuD49vWcRjI66o13gak4Z+m5KllAf929lAgxzAhRCzgdkA3bp1a6BobZOv3/2R529+hZKCMoSA06+azHX/mInZEjxk/fW/vUdZUXnATBoAydGc9s1I/7F9a2TePHfuGUz+7Ums/3Yz9hgbQyYOrFFLQHF8vL3nx7DSR7gNL+/t/YnbB55b43ioOASEz6NrQIcuLD7lTjYW7KfCcDMkLr1GtlKA13Z/x+FqHi+5rmJuXP0yr437Q0ARdYfZxhvjr2fNkd3sKsnBrluYt2lB1ZtHJcHKZ4LfdbLzMC5IG02RuxzDb5P/eP8qHtz834B+BHB+1wz6xqYC8HPeDh7cvPBoAynJrSjimhUvsOSUO3l119c4jcDvj4ZgYFxX/yWSn/N2BI3EXp4b6CEWSep0RRBCLBVCbAzyUyMtofQtHjRo+iilfFFKmSGlzEhKqn9Id1tnzbINPH71c+TnFOKucONyuvnila95+g/zQ16z7uuNwRV/C8BsNRGf3IE737gh4JwjLprx541i+OQTlOJvJDYU7AvLhGIg2VuaG3D8rM7DAyOq8eW8GZ3oW0jXhcbQ+HRGJ/YOUPx5ziK+ydkU4BHkNrwh30iEEIxM7Mkl6eM4p+tI3jvxZjpaY9DRsGomzEKnV0xK0FQOZk2vyuMTa7ZXmZYmpw4OGjNh0cw1XHIf2/JxUJlKPOU8tvljvs0JXtbTqpu5ptfkqn09RJxEpBd4j6VO5S+lnCKlHBzkZyGQI4RIBfD/DlzNUTQa/5n3YcBMvaLcxdK3vqe0qCzoNfHJHYIeb25MFhOz/j6DN3c/R2qPCEYMt2N6ODqFlXdHQwQNRBuV2Iuzu4zEqpkxCQ2bZsaqmZk3dEZY2SgPlOcHTXfhlQbbiw4GuSKQNEdHFk+6k/ljf899Qy/lo4m3cu+Q6UGVuVdKTqrmjlmJw2TjwWG/xaaZsesWbJoZi2bimt6T6d/haPqQrLIA3xXAN8P9KmdzUBdaATw/+hq6+99iKtdLjk1PYRY6p6cODbg+kjTU7PMxMBN4yP97Ye3NFQ3h4K7gQTsmk05+dkHQSlSX3nE+j8x6loqyQC+M5kQIwblzz6hzzULReFzWfQKfZ/2Ctw6/f4fJysXHFLAH32d2x8BzuSBtFMtztxNlsjA5eTAJ1vByPaVFJVIRJN+QjqihdOtCCMGguDQqnVuTgTl9TuP57V+gIRBCYEjJ/UMvDZli+cSkfiw55S6+O7SFCsPN+KR+JB+z0B1riQoZlGWEKLWpC420qMQax24bMI2dxTnsLzuMlBIhBN2jk7ix/5lhj7kpaKjyfwh4TwhxNbAXmA4ghMgA5kgpr/Hvfw/0BxxCiEzgainl5w28d7uj/5g+5GYeDliclUCnbh0D2pcWlnI46wipPTux/9csDCPyC7uaScPwBH5RdJPWrKmhmwMpJYsPrOFVf679E+K6cX2/0+kTkxqR+3d3dOKJjFk8uGkBmWVHEPh87N2GF6/fHDQqsRf/N/gi4izRIfvpE5N6XDJ/krkqwM4OvsR2V/SYWO/+qnNZjwlMTT2Bn3K3YdZ0JnYaUGdufYfZxlldQkfyXtNzMg9vWRhgy06LSqR/bBeWZW8IiJ3oEpUQcN8Ys503xl/P2vw97C3NpYejE0Pj0pvd7VgVc2lF7N28n+vH3E1FmZPKj80aZeXKv13C9NtqVoY6fDCfuRl/orSwjIqyCkxmEx53ZLI8pg/qytizR1KYV8yqL9aRl3kkoI3dYePJn+bRY3D7Wdh/eedXvLrz2xrmArtu4dVxcwMWO5uaEo/TV4dY6OwqycFteOkTm9pkicW2FmVx7YoXgppKzEJnTt+pQR8Auc4idhRn09keT7ojsuuAUkr+sWUR7+9b4dtH0suRwgtjrqXY7WTm8mersriahIZZM/FkxqygJrNIoso4tkHSB6bx5I/38++732LLiu0kpMTx27svZPJvAxNK/fuutyjMLcTrn3VHRPELiE2M4aHP/kzHLr5X3+vH3hVU+Xu9Brbo5iuxGGmcXleA4gdfUZb5O75i3rAZEZXHYbJVbfeKSWny+y3JWhvS08gtvbywfSnTuoyseuMwpMFDmxayJGstFv/bycAOXXl85BU1ZG9KhBDcOnAaV/eezI7ibFLscXT1m3RizHbeO+lm3t+3gvX5e+nu6MSl3caTFp1YR68tB6X8Wxk9h6Qzb9HdtbYpOlzsS2wWxNzSVGiaYMoVJ3PdP2fhiDtqMjhv7hk8temlqjQLlW3T+nVuVwu9WeX5QYOgDCSbCzObQaLI4vZ6avXRNwudNUd2M9mfFuG9vcv5LGsdLsNT9dDYWLCPBzYu4IFhvwnah8fw8u2hzazM20GSLZZpXUaSYo9rsOxxlmgygqTqiLc4mN17SsjrthQeYNGB1Ti9bqaknMDYjn2a3dRTHaX8WzlSSj5/5WveffRjig4X0SExhuw9h4KmQG4qNF2jY5cETv/dKbz6l3fIzTxMv1G9OfvaKUy5YiIbf9jC0je/87lsCoiJd/C3j26PmHwtgY7WWDwh3Cy7RIWoSNaGmJJ6Aouy1tRaWaz6jP6dvT8FvCW5pZdvczbj9Lqw6TUdBZxeN3NWvsTu0kOUe12Yhc4bu77jkRGXN2rqhHB5Y/d3vLR9GS5/TqWl2Rs4Makf84bOaDEPAGXzb+U8d9PLLHz+86CLqhFB+Kp+5WYe5nDWUdc4k8WE3WHjmZUP0rlXCgd35bB5+TYSUuMYOmkQWj1yxLcV7tvwAV8e3BCQB+aJjFmMqJYHpi0ipeSBTQv4LGtd0Myf8ZZoFk+6s8r3feqy+yl0B3dfPjN1GPeccGGNUov/2fMDz2/7ssbfFqCDOYrPJt8d0SIpuc4iLvjusQAzl1238MjwyxjTxA8jVcC9HZB/qJCFzzWf4rdGWfjL+7eSuf1gDcUP4HF5KCko5blbXgUgtWcyp152EsMnn9AuFT/AXYPO59yuI6uCk5Kssdw75JI2r/jBZz+/e9AFPDPqakYl9EJDYNfNROlWEiwOnq6WUgFgTMfeIWvwLsveyMObanqVf5b1S4DiB3AbHraFGUPQWKzM2x70YVPudfF1TgQLIdWBMvu0Ynau24NhNI/it9gtXP/01ThLKvCEMDFJQ7Lmy/URlqzlYtZM3D7wXG7qf1ZYud/bGkIIhsan8+zoqylxO1mbv4dok4Wh8d0DlOXcPqezIm87xe7ygJUCl/Tw+cFfuGXA2VWmIqsWXJUZyIBi7E2NVTcHfWzpiKqSni2B9jkFayMkpSU2MKFG/dF0jd4j8uU+vAAAEtpJREFUevB+znzO+N1kDh/Mx+UMbce12lvOP3tLwaKZiLNEtSvFfywOs42TOvVnRELPoLPkzlHxvDvhZux6cI8wXWjkV5RW7V/UbQw2rWY6CUFlfeRAN9q9Jbnct+EDZvzwJPese7tR3w5OTOoX9Gtp0kx1VnSLJEr5t2JK8kvRTREs+KxrzH70Cp5Z8SBR/kRsg0/sh8UWPKmcyWLi9KtOiZh8irZFojXG5yETZB4tEKTYj0bknpY6hKmpQ3w1dTWfOamDJZrHRlwR8JDdWpTFlcuf5dOsdewqyWFp9kauWfEvVh/e1ShyR5msPDbiCqJ0C9G6lSjdgkUzcWO/M+gdAbfacFELvq2Ub9/7iUevejZiWTktVjMX3XoOV93/2xrHpZTcdeY8Nny/GVf50TcAoQlGnHoC9y38k0rhoDhudpXk8Lvlz+P0uo7WANDMzO17GjO6nxjQfk/JIdbl7yXB6mBcxz6Yg5h85qx8iTX5uwOOd49O4r2Tbmk02Z1eF8tzt1FheBjTsTfxlvDSYDSUcBd8lfJvhXi9XqanXktRXuhi0I2JpmtceNNZ/P6xmUHPe9wePnn+cz57+WucZRUMGNOHi/44jT7D2/5CpqLp2VmczfPbv2RDwT6SrLH8rtckTk054bj7m/DFX4IGnAkEP5x2b9AHRmtCRfi2YQ7ty4toHn4hBDEJtRSzNpu44MazueDGsyMmk6L90CsmpVGLnsSa7UETtll1X7qL9oKy+bdCYuIdGN7GL22nhVg/MJl1xp1b50RCoWgV/Cb9xIDFYatm4oK00e1qEV4p/1aIxW4huXvjJgKz2Myk9khm+u3nYY2yIDSB0ATWKCvn3XBmu0rApmjbXNZjAud2zcCimYg2WbFoJianDOb6vqc3t2gRRZl9WiF/u/BRcvYGVlqqL2armWGnDKLwcDEnXTiWadedRnRsFBMvGcc37/6INCSTLh1P/9GRD49XKJoKTWjcNnAas/tMIbPsMKn2uIgtxrYklPJvZezZtJ/132zCXYtvfbgYXoMRU4Zw8R+n1TjeL6MX/TICE1kpFG2JWLOdgR26NrcYzYYy+7Qydq3fG9I2X1+8Hi/vPvLfRulLoVC0LpTyb0VsWbmdr9/5oVE9fQoj5C6qUChaFsrs00r46MlFvHzP27ic7pClGIUGIUqLhqTbgPBrpyoUiraDUv6tgKLDxcy/6z+12vmPuqiFH7RntVu47h+zGiacQqFolSjl30LZu3k/W1ZsJ7FzPGUlTswWU63KX0oZUu8LAVL63DlNZhMWu5n0gWnMuu9SBk8Y0EQjUCgULRml/FsYXq+XBy97ihWfrKrytTdbzRghTD11YbVbmHrlyRTkFnHi+aM5efo4zJbgidgUCkX7oUHKXwiRALwLdAf2ANOllPnHtBkGPA/EAl5gnpTy3Ybcty2z+MWlrFi0moryo4u6zhInmn58a/PdBnTlpudnN5Z4CoWijdBQb587gWVSyj7AMv/+sZQBV0opBwFnAE8IIRpeVbmNUZhXxNqvNrDgycVUlFXUOCclCE0jJsGBPcYedhpns9XENQ9d1hTiKhSKVk5Dlf95wGv+7deA849tIKXcJqXc7t/OAg4BSQ28b5tBSsmLd7zOb9Lm8JfzHiZzW/CiErpJ54kf/s7IqUPCzj9ispgYcvLAxhRXoVC0ERqq/JOllJXaKhtIrq2xEGI0YAF2NvC+bYalb3zHx899jrvCjbO0ImS72AQHaf26sPrLX/C4w0vqJoRg009bG0tUhULRhqjT5i+EWAoEKz9zT/UdKaUUQoRclRRCpAJvADOlDO6NLoSYDcwG6NatdScSc5ZV4HV7iO4QXWu79//xcZ1BW0LA3Cd/hxCi3gFeoWICFApF+6bOmb+UcoqUcnCQn4VAjl+pVyr3Q8H6EELEAouBe6SUK2q514tSygwpZUZSUuu0DBXkFvJ/5z7EBfEzuSjpan4//DZ2rA2sGlRJyZHSkOeqEPDKn99GSsmg8f2CN9GCl7obdGLw9gqFon3TULPPx0BleaeZwMJjGwghLMAC4HUp5QcNvF+LRkrJ7afey/8+X4fH7cXr8bLrl73cOumvHMnOD3rNqDOH192vAbn7D7N5+TZueOZqomLsmCy+lzaTxYQ9xs7QSYOwRVtB+Pz5rVEW7nn3FuXWqVAogtJQP/+HgPeEEFcDe4HpAEKIDGCOlPIa/7GJQKIQYpb/ullSynUNvHeLY9OPv5K9JxfvMTZ5j9vDkvnLuPzPFwdcc+nt57Jk/tK6A3MF5O7PY9KlJzJ/0z9Z8NQSdqzZRe8RPTn/hjNJ6prIph9/ZfWX64mJdzBpxngSUuIbcXQKhaIt0SDlL6U8DJwa5Pgq4Br/9pvAmw25T2vh4K6gVi9cTjf7fz1Q49iBHQf55PnPWfXFejRNw/DWnpTH6/bS159mOalrIrMfCSxrN3jCABWxq1AowkJF+DYivYd3RxqBStwaZWXguL5V+2uWrucv5z+Cx+0JeEsIhjXKyoQLRtO5V7B1d4VCoag/Svk3Ij1OSGfIyYNY/82mqghd3aTjiIti6pWTAN+6wKO/ezYgkOtYdJNObKKDmHgH5/7hdM6Zc1pTi69QKNoRSvk3Mn/76HbeeWgBS15ahqvcxbjzMrhq3m+JirEDkL3nEMX5JbX2IQR06taR17Y/3a4KSisUisihlH8jY7GaufKv07nyr9ODnrdF20La94UmsEfbsEVbuX/RXUrxKxSKJqNdKv/yUifrvtqIpmsMnzwYi83SZPcyDIP3Hl3Ih/9cTEl+Kf1G9SJ9YBq7N+zF6zn6ELDYLEz+zYlMnD6eEaeegG7Sm0wmhUKhaHfK//sPV/DwzGeqkqNJCX/94FZGTh0a0Hbz8q188+5PaLrGKb+ZcFxFzZ+/5VU+/fdXVTb+TT9txWq3kJyeRH5OIUIIPG4Pk387gVte/D2apiprKhSKpkdI2TLD/zMyMuSqVasa3I+Uku8/XMH7j39CfnYBuZmHA8wu1igrb+//FzHxjqpj/7r1VRa9sBRXuasqcOriW6cx694ZYd+7pKCU6Z2vDSjCommCky8dz/k3nEVe5mH6jepNcnrrjGhWKBQtCyHEaillRl3t2vzM/82/f8C7jy6kopakaULA9x+u5KxrfCELO9btZtG/vjyaU19CRZmL9x/9mCmXTaRr385h3fvgrpygFbgMQ7Jj7R4Gju0b4kqFQqFoWtq0jaG0sJR3HlpQq+IH8Li9lBeXV+0v/2QVbpcnoJ2UkhWLVod9/+T0JNwVgf0ITdBjcFrY/SgUCkVj06aV/671+zBb685to+saGacftflbrJaglbM0TQurv0piE2OYcvlJWKNqLihbbBZ+c/eFYfejUCgUjU2bVv4JqXF4gszgq2OLtnLG1ZNJH3h0Jn7y9HFoQbJkSuCki8bUS4abnp/NBTeehd1hQwjoPjiNeYvvovewHvXqR6FQKBqTNr/ge8vE/+PXn7fjcR1No2C2mhh8Yn/ikuM4fdYkRkwJrI712Stf8fQf5qOZdATg9Rrc/spcJk0/8bjkkFJieA3lwqlQKJqUcBd827zyLzpSzLwZT7Dh+y2YzDq6WeeGZ69h8owJdV5bkFvIz0vWIjTBmLNHEJsQ02B5FAqFoilRyv8Y8nMKKM4vpUvvFDX7VigUbRbl6nkM8clxxCfHNbcYCoVC0SJo0wu+oZBS0lLfeBQKhSIStCvlv2v9XmZ0nc1p+nRO06czo+vv2b1xX3OLpVAoFBGn3Sj/0uIyrht5B4ezjtbSPZx1hDkjbqe4oITSojL1NqBQKNoN7cbm//pf3wuaStnwGFzc6WoEgqS0RG589hpGnVF3UXWFQqFozbSbmf+ONbtCnjM8Bl6Pl+zdh7j34sfYumpnBCVTKBSKyNNulH+fkeGlY3aVu3j7wY+aWBqFQqFoXtqN8p9533Q0U93DlRIyt2ZFQCKFQqFoPhqk/IUQCUKIL4UQ2/2/44O0SRdCrBFCrBNCbBJCzGnIPY8Xe7SdF9Y+SlJaYq3tNF2j36jeEZJKoVAomoeGzvzvBJZJKfsAy/z7x3IQGCelHAaMAe4UQoSXEL+R6T6oG//Z+y++8L7HF973uOS2c7FFWWu0sdpVxk2FQtH2aajyPw94zb/9GnD+sQ2klC4pZWVCfWsj3LPBCCEQQnDtw5dz7SOXk9w9CbvDRsZpQ3nyx/vp2ie1uUVUKBSKJqVBuX2EEAVSyjj/tgDyK/ePaZcGLAZ6A7dLKZ+tq+/GyO0jpWTZW9/z4T8/oehICaPPHM5lf76Yjp0TGtSvQqFQtFQaLbGbEGIpkBLk1D3Aa9WVvRAiX0oZYPevdr4z8F9gmpQyJ8j52cBsgG7duo3cu3dvXfLXyvy73mLhM5/i9Ffy0k06MQkOXtrwOHFJHRrUt0KhULREwlX+dZpgpJRTpJSDg/wsBHKEEKn+G6YCh+roKwvYCJwU4vyLUsoMKWVGUlLDCpoX5hXx0ROLqxQ/gNfjpayojIXPftagvhUKhaK101D7+8fATP/2TGDhsQ2EEF2FEHb/djwwAdjawPvWya71e7HYAksuupxu1i7b0NS3VygUihZNQ5X/Q8BUIcR2YIp/HyFEhhBivr/NAGClEOIX4FvgMSllk2vfxM4JQUs4apogtWdyU99eoVAoWjQNyu0jpTwMnBrk+CrgGv/2l8CQhtzneOjWvwu9hnVn2+qdNUs42sxcdPM5kRZHoVAoWhTN7nbZlPz9kzsZfsoJmK0mbFFWOiTFcvdbN9N7uCqerlAo2jdtOqtnbEIMD3x6D4V5RZQUlJLSoxO6rko4KhQKRZtW/pV06BhLh46xzS2GQqFQtBjatNlHoVAoFMFRyl+hUCjaIUr5KxQKRTtEKX+FQqFohyjlr1AoFO0QpfwVCoWiHaKUv0KhULRDlPJXKBSKdohS/gqFQtEOaVAlr6ZECJEL1FbNpSOQFyFxmpq2Mpa2Mg5QY2mpqLHUTbqUss6CKC1W+deFEGJVONVqWgNtZSxtZRygxtJSUWNpPJTZR6FQKNohSvkrFApFO6Q1K/8Xm1uARqStjKWtjAPUWFoqaiyNRKu1+SsUCoXi+GnNM3+FQqFQHCetRvkLIRKEEF8KIbb7f8cHaZMuhFgjhFgnhNgkhJjTHLLWRZhjGSaEWO4fx3ohxKXNIWtthDMOf7vPhBAFQohFkZaxLoQQZwghtgohdggh7gxy3iqEeNd/fqUQonvkpQyPMMYy0f/98AghLm4OGcMljLH8UQix2f/dWCaESG8OOesijHHMEUJs8OusH4QQAyMmnJSyVfwAjwB3+rfvBB4O0sYCWP3bDmAP0Lm5ZT/OsfQF+vi3OwMHgbjmlr2+4/CfOxWYBixqbpmPkUsHdgI9/f87vwADj2kzF/iXf3sG8G5zy92AsXQHhgCvAxc3t8wNHMspQJR/+7qW+LmEOY7YatvnAp9FSr5WM/MHzgNe82+/Bpx/bAMppUtKWeHftdJy32zCGcs2KeV2/3YWcAioM3AjwtQ5DgAp5TKgOFJC1YPRwA4p5S4ppQt4B9+YqlN9jB8ApwohRARlDJc6xyKl3COlXA8YzSFgPQhnLF9LKcv8uyuArhGWMRzCGUdRtd1oIGKLsC1VOQYjWUp50L+dDSQHaySESBNCrAf245uJZkVKwHoQ1lgqEUKMxjdz2NnUgtWTeo2jBdIF3/9JJZn+Y0HbSCk9QCGQGBHp6kc4Y2kt1HcsVwOfNqlEx0dY4xBC/EEIsRPfm/SNEZKtZRVwF0IsBVKCnLqn+o6UUgohgj4hpZT7gSFCiM7Af4UQH0gpcxpf2tppjLH4+0kF3gBmSikjPmNrrHEoFE2BEOJyIAM4ubllOV6klM8Czwohfgv8GZgZifu2KOUvpZwS6pwQIkcIkSqlPOhXiIfq6CtLCLEROAnf63pEaYyxCCFigcXAPVLKFU0kaq005mfSAjkApFXb7+o/FqxNphDCBHQADkdGvHoRzlhaC2GNRQgxBd8k5ORq5t6WRH0/k3eA55tUomq0JrPPxxx9Is4EFh7bQAjRVQhh92/HAxOArRGTMHzCGYsFWAC8LqWM+MMrTOocRwvnf0AfIUQP/997Br4xVaf6GC8GvpL+1bkWRjhjaS3UORYhxHDgBeBcKWVLnXSEM44+1XbPBrZHTLrmXhGvx8p5IrDM/8dZCiT4j2cA8/3bU4H1+FbV1wOzm1vuBozlcsANrKv2M6y5Za/vOPz73wO5QDk+u+fpzS17NdnOArbhW0+5x3/sPnxKBcAGvA/sAH4Geja3zA0Yyyj/378U39vLpuaWuQFjWQrkVPtufNzcMh/nOJ4ENvnH8DUwKFKyqQhfhUKhaIe0JrOPQqFQKBoJpfwVCoWiHaKUv0KhULRDlPJXKBSKdohS/gqFQtEOUcpfoVAo2iFK+SsUCkU7RCl/hUKhaIf8P9DDhxOGEa62AAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_2d_reduction_pca(small_sample_categories)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting Raw Data...\n", "Training data shape: (1456, 12661)\n", "Test data shape: (968, 12661)\n", "Fitting the Reducer...\n", "Reducing the Data...\n", "Reduced Training Data\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Reduced Test Data\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_2d_reduction_pca(religious_categories)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting Raw Data...\n", "Training data shape: (2936, 47493)\n", "Test data shape: (1955, 47493)\n", "Fitting the Reducer...\n", "Reducing the Data...\n", "Reduced Training Data\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Reduced Test Data\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_2d_reduction_pca(computer_categories)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting Raw Data...\n", "Training data shape: (2243, 19179)\n", "Test data shape: (1494, 19179)\n", "Fitting the Reducer...\n", "Reduced Training Data\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_2d_reduction_spectral(small_sample_categories)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting Raw Data...\n", "Training data shape: (1456, 12661)\n", "Test data shape: (968, 12661)\n", "Fitting the Reducer...\n", "Reduced Training Data\n" ] }, { "data": { "image/png": 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\n", 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