ml model update

machine_learning/model.py

@@ -0,0 +1,172 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix
+from keras.models import Sequential
+from keras.optimizers import SGD
+from keras.layers import Dense, BatchNormalization, Activation, Dropout
+from keras.callbacks import EarlyStopping, Callback
+from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
+from keras.models import load_model
+from IPython.display import clear_output
+
+user_object = dict()
+
+user_object["fake"] = pd.read_csv("Dataset/fake_twitter_accounts.csv")
+user_object["legit"] = pd.read_csv("Dataset/real_twitter_accounts.csv")
+
+user_object["legit"] = user_object["legit"].drop(
+    ["id", "name", "screen_name", "created_at", "lang", "location", "default_profile", "default_profile_image",
+     "geo_enabled", "profile_image_url", "profile_banner_url", "profile_use_background_image",
+     "profile_background_image_url_https", "profile_text_color", "profile_image_url_https",
+     "profile_sidebar_border_color", "profile_background_tile", "profile_sidebar_fill_color",
+     "profile_background_image_url", "profile_background_color", "profile_link_color", "utc_offset", "protected",
+     "verified", "dataset", "updated", "description"], axis=1)
+
+user_object["fake"] = user_object["fake"].drop(
+    ["id", "name", "screen_name", "created_at", "lang", "location", "default_profile", "default_profile_image",
+     "geo_enabled", "profile_image_url", "profile_banner_url", "profile_use_background_image",
+     "profile_background_image_url_https", "profile_text_color", "profile_image_url_https",
+     "profile_sidebar_border_color", "profile_background_tile", "profile_sidebar_fill_color",
+     "profile_background_image_url", "profile_background_color", "profile_link_color", "utc_offset", "protected",
+     "verified", "dataset", "updated", "description"], axis=1)
+
+user_object["legit"] = user_object["legit"].values
+user_object["fake"] = user_object["fake"].values
+
+for index in range(len(user_object["legit"])):
+    if type(user_object["legit"][index][5]) == str:
+        user_object["legit"][index][5] = 1
+
+    if type(user_object["legit"][index][6]) == str:
+        user_object["legit"][index][6] = 1
+
+for index in range(len(user_object["fake"])):
+    if type(user_object["fake"][index][5]) == str:
+        user_object["fake"][index][5] = 1
+
+    if type(user_object["fake"][index][6]) == str:
+        user_object["fake"][index][6] = 1
+
+user_object["legit"] = user_object["legit"].astype(np.float64)
+user_object["fake"] = user_object["fake"].astype(np.float64)
+
+where_nans = np.isnan(user_object["legit"])
+user_object["legit"][where_nans] = 0
+
+where_nans = np.isnan(user_object["fake"])
+user_object["fake"][where_nans] = 0
+
+X = np.zeros((len(user_object["fake"]) + len(user_object["legit"]), 7))
+Y = np.zeros(len(user_object["fake"]) + len(user_object["legit"]))
+
+for index in range(len(user_object["legit"])):
+    X[index] = user_object["legit"][index] / max(user_object["legit"][index])
+    Y[index] = -1
+
+for index in range(len(user_object["fake"])):
+    bound = max(user_object["fake"][index])
+    if bound == 0:
+        bound = 1
+
+    X[len(user_object["legit"]) + index] = user_object["fake"][index] / bound  # Normalizing Data [0 <--> 1]
+    Y[len(user_object["legit"]) + index] = 1
+
+X_train_data, X_test_data, y_train_data, y_test_data = train_test_split(X, Y,
+                                                                        test_size=0.24, random_state=42)
+
+early_stopping = EarlyStopping(monitor='val_loss', patience=2)
+
+
+class PlotLearning(Callback):
+    def on_train_begin(self, logs={}):
+        self.i = 0
+        self.x = []
+        self.losses = []
+        self.val_losses = []
+        self.acc = []
+        self.val_acc = []
+        self.fig = plt.figure()
+
+        self.logs = []
+
+    def on_epoch_end(self, epoch, logs={}):
+        self.logs.append(logs)
+        self.x.append(self.i)
+        self.losses.append(logs.get('loss'))
+        self.val_losses.append(logs.get('val_loss'))
+        self.acc.append(logs.get('acc'))
+        self.val_acc.append(logs.get('val_acc'))
+        self.i += 1
+        f, (ax1, ax2) = plt.subplots(1, 2, sharex=True)
+
+        clear_output(wait=True)
+
+        ax1.set_yscale('Log')
+        ax1.plot(self.x, self.losses, label="loss")
+        ax1.plot(self.x, self.val_losses, label="val_loss")
+        ax1.legend()
+
+        ax2.plot(self.x, self.acc, label="accuracy")
+        ax2.plot(self.x, self.val_acc, label="validation accuracy")
+        ax2.legend()
+
+        plt.show();
+
+
+plot = PlotLearning()
+
+model = Sequential([
+    BatchNormalization(),
+
+    Dense(16, activation="relu", kernel_regularizer="l2"),
+    BatchNormalization(),
+    Dense(8, activation="relu", kernel_regularizer="l2"),
+    BatchNormalization(),
+    Dense(1, activation="tanh"),
+])
+
+model.build((None, X.shape[1]))
+model.summary()
+model.compile(
+    optimizer="adadelta",
+    loss="binary_crossentropy",
+    metrics=["acc"]
+)
+
+model.fit(X_train_data, y_train_data, epochs=2000, validation_data=(X_test_data, y_test_data), shuffle=True,
+          batch_size=100,
+          callbacks=[early_stopping, plot])
+
+prediction = model.predict(X_test_data).T[0]
+
+for index in range(len(prediction)):
+    prediction[index] = -1 if prediction[index] < 0 else 1
+
+
+def plot_confusion_matrix(cm, title='CONFUSION MATRIX', cmap=plt.cm.Reds):
+    target_names = ['Fake', 'Real']
+    plt.imshow(cm, interpolation='nearest', cmap=cmap)
+    plt.title(title)
+    plt.colorbar()
+    tick_marks = np.arange(len(target_names))
+    plt.xticks(tick_marks, target_names, rotation=45)
+    plt.yticks(tick_marks, target_names)
+    plt.tight_layout()
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label')
+
+
+mat = confusion_matrix(y_test_data, prediction)
+print(mat)
+
+plot_confusion_matrix(mat)
+
+_, train_accuracy = model.evaluate(X_train_data, y_train_data)
+_, validation_accuracy = model.evaluate(X_test_data, y_test_data)
+print("Train Accuracy:", train_accuracy)
+print("Validation Accuracy:", validation_accuracy)
+
+model.save('keras_model/model_twitter.hdf5')
+frozen_model = load_model("keras_model/model_twitter.hdf5")