VincentTatan

def create_logistic_regressions(X_train,y_train,figsize=(10,10)):
  logreg = LogisticRegression(solver='lbfgs')
  logreg.fit(X_train, y_train)

  coefficients = logreg.coef_
  intercept = logreg.intercept_
  
  df_logreg = pd.DataFrame({'Feature':X_train.columns,'Coef':logreg.coef_[0]})
  fig, ax = plt.subplots(figsize=figsize)
  sns.barplot(x="Coef", y="Feature", data=df_logreg, ax=ax)

VincentTatan

def create_and_visualize_tree(X_train,y_train,max_depth=3):
  decision_tree = DecisionTreeClassifier(max_depth=max_depth, min_samples_leaf=1,random_state=1)
  decision_tree = decision_tree.fit(X_train, y_train)
  tree_str = export_graphviz(decision_tree, feature_names=X_train.columns, 
                                     filled=True, out_file=None)
  graph = pydotplus.graph_from_dot_data(tree_str)  
  graph.write_png('dt.png')
  display(Image('dt.png'))
  return decision_tree

VincentTatan

def plot_confusion_matrix(cm_list,target_names,title_list,cmap=None,normalize=True,float_format_str='{:,.2f}'):
    plt.figure(figsize=(10,5))
    print('{}_count={:d}\n{}_count={:d}'.format(target_names[0],cm_list[0][0].sum(),target_names[1],cm_list[0][1].sum()))
    stats_list = []

    for i in range(len(cm_list)):
      model_name = title_list[i]
      cm = cm_list[i]

      actual_phishy= cm[0]

VincentTatan

def take_roc_curve(X_test,model):
  y_preds = model.predict_proba(X_test)
  preds = y_preds[:,1]

  fpr, tpr, _ = metrics.roc_curve(y_test, preds)
  precision, recall, _ = metrics.precision_recall_curve(y_test, preds)
  auc_score = metrics.auc(fpr, tpr)

  plt.figure(figsize=(10,5))

VincentTatan

def generate_logs_from_classifiers(classifiers):
  log_cols=["Classifier", "Accuracy", "Log Loss"]
  log = pd.DataFrame(columns=log_cols)

  for clf in classifiers:
      name = clf.__class__.__name__
      print('Processing {} classifier'.format(name))

      clf.fit(X_train, y_train)
      train_predictions = clf.predict(X_test)

VincentTatan

from tensorflow.keras.preprocessing.text import Tokenizer

sentences = [
  'I eat chicken',
  'I do not eat fish',
  'Did you eat fish?'
]

tokenizer = Tokenizer(num_words = 100, oov_token="<OOV>")
tokenizer.fit_on_texts(sentences)

VincentTatan

from tensorflow.keras.preprocessing.sequence import pad_sequences

sequences = tokenizer.texts_to_sequences(sentences)

padded = pad_sequences(sequences, maxlen=5)
print("\nThe Word Index = " , word_index)
print("\nThe Sequences = " , sequences)
print("\nThe Padded Sequences:")
print(padded)

VincentTatan

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(24, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])

VincentTatan

def fixed_width_cut(df,feature,labels=['Low','Medium','High']):
  feature_slice, retbins = pd.cut(df[feature], len(labels) ,retbins=True, labels=labels)
  retbins = [ '%.2f' % elem for elem in retbins ]
  return feature_slice,retbins

def quartile_cut(df,feature,labels=['Low','Medium','High']):
  feature_slice, retbins = pd.qcut(df[feature], q=len(labels),retbins=True,labels=labels)
  retbins = [ '%.2f' % elem for elem in retbins ]
  return feature_slice,retbins