update dockerfile, data_expl, add nn_train

Dockerfile

@@ -7,9 +7,13 @@ WORKDIR /app
 
 COPY ./data_expl.py ./
 COPY ./googleplaystore.csv ./
+COPY ./nn_train.py ./
 
 RUN pip3 install pandas
 RUN pip3 install numpy
+RUN pip3 install tensorflow
+RUN pip3 install keras
+RUN pip3 install sklearn
 
-
-CMD python3 data_expl.py
+CMD python3 data_expl.py
+CMD python3 nn_train.py

data_expl.py

@@ -14,6 +14,10 @@ for column in to_lowercase:
 
 data["Installs"] = data["Installs"].replace({'\+': ''}, regex=True)
 data["Installs"] = data["Installs"].replace({',': ''}, regex=True)
+data["Price"] = data["Price"].replace({'\$': ''}, regex=True)
+
+data["Genres"] = data["Genres"].astype('category')
+data["Genres_numeric_value"] = (data["Genres"].cat.codes).astype('float32')
 
 # normalizing numbers
 data["Reviews"] = pd.to_numeric(data["Reviews"], errors='coerce')
@@ -26,7 +30,13 @@ max_value = data["Installs"].max()
 min_value = data["Installs"].min()
 data["Installs"] = (data["Installs"] - min_value) / (max_value - min_value)
 
-#print(data)
+data["Rating"] = np.asarray(data["Rating"]).astype('float32')
+data["Reviews"] = np.asarray(data["Reviews"]).astype('float32')
+data["Installs"] = np.asarray(data["Installs"]).astype('float32')
+data["Price"] = np.asarray(data["Price"]).astype('float32')
+
+
+print(data)
 
 
 # splitting into sets

nn_train.py

@@ -0,0 +1,64 @@
+
+import pandas as pd
+import numpy as np
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from sklearn.preprocessing import LabelEncoder
+from keras.utils import np_utils
+
+# reading data
+def read_data():
+    all_data = []
+    for name in ['train', 'test', 'validate']:
+        all_data.append(pd.read_csv(f'apps_{name}.csv', header=0))
+    return all_data
+
+train_set, test_set, validate_set = read_data()
+train_set = train_set.drop(columns=["Unnamed: 0"])
+test_set = test_set.drop(columns=["Unnamed: 0"])
+validate_set = validate_set.drop(columns=["Unnamed: 0"])
+numeric_columns = ["Rating", "Reviews", "Installs", "Price", "Genres_numeric_value"]
+
+# train set set-up
+x_train_set = train_set[numeric_columns]
+y_train_set = train_set["Category"]
+encoder = LabelEncoder()
+encoder.fit(y_train_set)
+encoded_Y = encoder.transform(y_train_set)
+dummy_y = np_utils.to_categorical(encoded_Y)
+
+# validation set set-up
+x_validate_set = validate_set[numeric_columns] 
+y_validate_set = validate_set["Category"]
+encoder = LabelEncoder()
+encoder.fit(y_validate_set)
+encoded_Yv = encoder.transform(y_validate_set)
+dummy_yv = np_utils.to_categorical(encoded_Yv)
+
+#test set set-up
+x_test_set = test_set[numeric_columns]
+y_test_set = test_set["Category"]
+y_class_names = train_set["Category"].unique()
+encoder = LabelEncoder()
+encoder.fit(y_test_set)
+encoded_Ytt = encoder.transform(y_test_set)
+dummy_ytt = np_utils.to_categorical(encoded_Ytt)
+
+# model definition
+number_of_classes = 33
+number_of_features = 5
+model = Sequential()
+model.add(Dense(number_of_classes, activation='relu'))
+model.add(Dense(number_of_classes, activation='softmax',input_dim=number_of_features))
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
+model.fit(x_train_set, dummy_y, epochs=200, validation_data=(x_validate_set, dummy_yv))
+#model.save("my_model/")
+
+#model predictions
+#model = keras.models.load_model('my_model')
+yhat = model.predict(x_test_set)
+f = open("results.txt", "w")
+for numerator, single_pred in enumerate(yhat):
+    f.write(f"PREDICTED: {sorted(y_class_names)[np.argmax(single_pred)]}, ACTUAL: {y_test_set[numerator]} {sorted(y_class_names)[np.argmax(single_pred)] == y_test_set[numerator]}\n")
+f.close()