update neural networks typing and add new preprocessing (add actual normalization and correctly rename nrormalization to mimax)

gamestonk_terminal/config_neural_network_models.py

@@ -23,7 +23,7 @@ MultiLayer_Perceptron = [
     {"Dense": {"units": 100, "activation": "relu"}},
     {"Dense": {"units": 80, "activation": "relu"}},
     {"Dense": {"units": 30, "activation": "relu"}},
-    {"Dense": {"activation": "linear"}},
+    {"Dense": {"activation": "relu"}},
 ]
 
 Recurrent_Neural_Network = [
@@ -31,11 +31,11 @@ Recurrent_Neural_Network = [
     {"SimpleRNN": {"units": 50, "activation": "linear", "return_sequences": True}},
     {"Dropout": {"rate": 0.2}},
     {"SimpleRNN": {"units": 21, "activation": "linear", "return_sequences": False}},
-    {"Dense": {"activation": "linear"}},
+    {"Dense": {"activation": "relu"}},
 ]
 
 Long_Short_Term_Memory = [
     {"LSTM": {"units": 25, "activation": "tanh", "return_sequences": True}},
     {"LSTM": {"units": 15, "activation": "tanh", "return_sequences": False}},
-    {"Dense": {"activation": "linear"}},
+    {"Dense": {"activation": "relu"}},
 ]

gamestonk_terminal/prediction_techniques/README.md

@@ -153,7 +153,7 @@ Facebook's Prophet:
 
 ## mlp <a name="mlp"></a>
 ```
-usage: mlp [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [--epochs N_EPOCHS] [-p {normalization,standardization,none}]
+usage: mlp [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [--epochs N_EPOCHS] [-p {minmax, normalization,standardization,none}]
 [-o {adam,adagrad,adadelta,adamax,ftrl,nadam,optimizer,rmsprop,sgd}] [-l {mae,mape,mse,msle}] [-e S_END_DATE] [--loops N_LOOPS]
 ```
 MulitLayer Perceptron:
@@ -161,7 +161,7 @@ MulitLayer Perceptron:
   * -i : number of days to use for prediction. Default 40.
   * -j : number of jumps in training data. Default 1.
   * --epochs : number of training epochs. Default 200.
-  * -p : pre-processing data. Default normalization.
+  * -p : pre-processing data. Default minmax.
   * -o : optimization technique. Default adam.
   * -l : loss function. Default mae.
   * -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
@@ -183,7 +183,7 @@ MultiLayer_Perceptron \
         {'Dense':
                 {'units':30, 'activation':'relu'} },
         {'Dense':
-                {'activation':'linear'} }]
+                {'activation':'relu'} }]
 ```
 
 ![mlp](https://user-images.githubusercontent.com/25267873/108604944-d2025400-73a8-11eb-9ab6-52972160cd2a.png)
@@ -220,7 +220,7 @@ Recurrent_Neural_Network \
         {'SimpleRNN':
                 {'units':21, 'activation':'linear', 'return_sequences':False} },
         {'Dense':
-                {'activation':'linear'} }]
+                {'activation':'relu'} }]
 ```
 
 ![rnn](https://user-images.githubusercontent.com/25267873/108604940-d0d12700-73a8-11eb-837e-a5aa128942d9.png)
@@ -260,7 +260,7 @@ Long_Short_Term_Memory \
         {'LSTM':
                 {'units':15, 'activation':'tanh', 'return_sequences':False} },
         {'Dense':
-                {'activation':'linear'} }]
+                {'activation':'relu'} }]
 ```
 
 ![lstm](https://user-images.githubusercontent.com/25267873/108604943-d2025400-73a8-11eb-83c5-edb4a2121cba.png)

gamestonk_terminal/prediction_techniques/ets_view.py

@@ -2,7 +2,7 @@
 __docformat__ = "numpy"
 
 import argparse
-from typing import List
+from typing import List, Union
 import datetime
 import matplotlib.pyplot as plt
 import numpy as np
@@ -49,7 +49,7 @@ def check_valid_seasonal(seasonal: str) -> str:
     )
 
 
-def exponential_smoothing(other_args:List[str], s_ticker:str, df_stock:pd.DataFrame):
+def exponential_smoothing(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
     """
     Perform exponential smoothing forecasting
     Parameters
@@ -409,12 +409,14 @@ def exponential_smoothing(other_args:List[str], s_ticker:str, df_stock:pd.DataFr
         print("")
 
 
-def get_exponential_smoothing_model(data:pd.Series, trend, seasonal, seasonal_periods):
+def get_exponential_smoothing_model(
+    data: Union[pd.Series, np.ndarray], trend, seasonal, seasonal_periods
+):
     """
     Perform exponential smoothing
     Parameters
     ----------
-    data: pd.Series
+    data: Union[pd.Series, np.ndarray]
         Series of closing values
     trend: str
         Trend component.  One of [N, A, Ad]

gamestonk_terminal/prediction_techniques/neural_networks_view.py

@@ -1,4 +1,8 @@
+""" Neural Networks View"""
+__docformat__ = "numpy"
+
 import argparse
+from typing import List, Any
 import datetime
 import os
 import traceback
@@ -8,7 +12,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 from pandas.plotting import register_matplotlib_converters
 from TimeSeriesCrossValidation import splitTrain
-from sklearn.preprocessing import MinMaxScaler, StandardScaler
+from sklearn.preprocessing import MinMaxScaler, StandardScaler, Normalizer
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import LSTM, SimpleRNN, Dense, Dropout
 
@@ -44,7 +48,9 @@ ORIGINAL_TF_XLA_FLAGS = os.environ.get("TF_XLA_FLAGS")
 ORIGINAL_TF_FORCE_GPU_ALLOW_GROWTH = os.environ.get("TF_FORCE_GPU_ALLOW_GROWTH")
 
 
-def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
+def build_neural_network_model(
+    Recurrent_Neural_Network: List[Any], n_inputs: int, n_days: int
+):
     model = Sequential()
 
     for idx_layer, d_layer in enumerate(Recurrent_Neural_Network):
@@ -91,9 +97,24 @@ def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
     return model
 
 
-def _parse_args(prog, description, l_args):
-    """Create an argparser and parse l_args. Will print help if user requests it.
-    :return: ns_parser"""
+def _parse_args(prog: str, description: str, other_args: List[str]):
+    """
+    Create an argparser and parse other_args. Will print help if user requests it.
+    Parameters
+    ----------
+    prog: str
+        Program for argparser
+    description: str
+        Description for argparser
+    other_args
+        Argparse arguments to pass
+    Returns
+    -------
+    ns_parser: argparse.Namespace
+
+        Parsed argument parser
+    """
+
     parser = argparse.ArgumentParser(
         prog=prog,
         description=description,
@@ -140,8 +161,8 @@ def _parse_args(prog, description, l_args):
         "--pp",
         action="store",
         dest="s_preprocessing",
-        default="normalization",
-        choices=["normalization", "standardization", "none"],
+        default="minmax",
+        choices=["normalization", "standardization", "minmax", "none"],
         help="pre-processing data.",
     )
     parser.add_argument(
@@ -233,7 +254,7 @@ def _parse_args(prog, description, l_args):
     )
 
     try:
-        ns_parser = parse_known_args_and_warn(parser, l_args)
+        ns_parser = parse_known_args_and_warn(parser, other_args)
         if not ns_parser:
             return None
 
@@ -276,10 +297,27 @@ def _restore_env():
     restore("TF_FORCE_GPU_ALLOW_GROWTH", ORIGINAL_TF_FORCE_GPU_ALLOW_GROWTH)
 
 
-def _setup_backtesting(df_stock, ns_parser):
-    """Set up backtesting if enabled
-    :return: (df_stock, df_future), where df_future is None if s_end_date is not set.
-    :raises Exception: if configuration is invalid"""
+def _setup_backtesting(df_stock: pd.DataFrame, ns_parser: argparse.Namespace):
+    """
+    Setup backtesting when end data is specified
+    Parameters
+    ----------
+    df_stock: pd.DataFrame
+        Stock data
+    ns_parser: argparse.Namespace
+        Parsed arguments
+    Returns
+    -------
+    df_stock: pd.DataFrame
+        Data before end date
+    df_future: pd.DataFrame
+        Data after end date
+    Raises
+    -------
+    Exception
+        If configuration is invalid
+    """
+
     df_future = None
     if ns_parser.s_end_date:
         if ns_parser.s_end_date < df_stock.index[0]:
@@ -319,11 +357,16 @@ def _preprocess_split(df_stock, ns_parser):
         stock_train_data = scaler.fit_transform(
             np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
         )
-    elif ns_parser.s_preprocessing == "normalization":
+    elif ns_parser.s_preprocessing == "minmax":
         scaler = MinMaxScaler()
         stock_train_data = scaler.fit_transform(
             np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
         )
+    elif ns_parser.s_preprocessing == "normalization":
+        scaler = Normalizer()
+        stock_train_data = scaler.fit_transform(
+            np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
+        )
     else:  # No pre-processing
         stock_train_data = np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
 
@@ -343,8 +386,10 @@ def _preprocess_split(df_stock, ns_parser):
 
 def _rescale_data(df_stock, ns_parser, scaler, yhat, idx_loop):
     """Re-scale the data back and return the prediction dataframe."""
-    if (ns_parser.s_preprocessing == "standardization") or (
-        ns_parser.s_preprocessing == "normalization"
+    if (
+        (ns_parser.s_preprocessing == "standardization")
+        or (ns_parser.s_preprocessing == "normalization")
+        or (ns_parser.s_preprocessing == "minmax")
     ):
         y_pred_test_t = scaler.inverse_transform(yhat.tolist())
     else:
@@ -699,10 +744,25 @@ def _plot_and_print_results(
             print("")
 
 
-def mlp(l_args, s_ticker, df_stock):
+def mlp(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
+    """
+    Train a multi-layer perceptron model
+    Parameters
+    ----------
+    other_args: List[str]
+        Argparse Arguments
+    s_ticker: str
+        Ticker
+    df_stock: pd.DataFrame
+        Loaded stock dataframe
+
+    Returns
+    -------
+
+    """
     try:
         ns_parser = _parse_args(
-            prog="mlp", description="""Multilayer Perceptron. """, l_args=l_args
+            prog="mlp", description="""Multilayer Perceptron. """, other_args=other_args
         )
         if not ns_parser:
             return
@@ -768,10 +828,12 @@ def mlp(l_args, s_ticker, df_stock):
         _restore_env()
 
 
-def rnn(l_args, s_ticker, df_stock):
+def rnn(other_args: List[str], s_ticker, df_stock):
     try:
         ns_parser = _parse_args(
-            prog="rnn", description="""Recurrent Neural Network. """, l_args=l_args
+            prog="rnn",
+            description="""Recurrent Neural Network. """,
+            other_args=other_args,
         )
         if not ns_parser:
             return
@@ -839,10 +901,12 @@ def rnn(l_args, s_ticker, df_stock):
         _restore_env()
 
 
-def lstm(l_args, s_ticker, df_stock):
+def lstm(other_args: List[str], s_ticker, df_stock):
     try:
         ns_parser = _parse_args(
-            prog="lstm", description="""Long-Short Term Memory. """, l_args=l_args
+            prog="lstm",
+            description="""Long-Short Term Memory. """,
+            other_args=other_args,
         )
         if not ns_parser:
             return

gamestonk_terminal/prediction_techniques/pred_controller.py

@@ -15,7 +15,7 @@ from gamestonk_terminal.prediction_techniques import (
     arima_view,
     ets_view,
     knn,
-    neural_networks,
+    neural_networks_view,
     regression,
     sma,
 )
@@ -159,15 +159,15 @@ class PredictionTechniquesController:
 
     def call_mlp(self, other_args: List[str]):
         """Process mlp command"""
-        neural_networks.mlp(other_args, self.ticker, self.stock)
+        neural_networks_view.mlp(other_args, self.ticker, self.stock)
 
     def call_rnn(self, other_args: List[str]):
         """Process rnn command"""
-        neural_networks.rnn(other_args, self.ticker, self.stock)
+        neural_networks_view.rnn(other_args, self.ticker, self.stock)
 
     def call_lstm(self, other_args: List[str]):
         """Process lstm command"""
-        neural_networks.lstm(other_args, self.ticker, self.stock)
+        neural_networks_view.lstm(other_args, self.ticker, self.stock)
 
     if gtff.ENABLE_FBPROPHET:
 

gamestonk_terminal/prediction_techniques/pred_helper.py

@@ -1,3 +1,6 @@
+""" Prediction helper functions """
+__docformat__ = "numpy"
+
 import numpy as np
 import pandas as pd
 from colorama import Fore, Style
@@ -11,6 +14,7 @@ from gamestonk_terminal import feature_flags as gtff
 
 
 def price_prediction_color(val: float, last_val: float) -> str:
+    """Set prediction to be a colored string"""
     if float(val) > last_val:
         color = Fore.GREEN
     else:
@@ -19,6 +23,7 @@ def price_prediction_color(val: float, last_val: float) -> str:
 
 
 def print_pretty_prediction(df_pred: pd.DataFrame, last_price: float):
+    """Print predictions"""
     if gtff.USE_COLOR:
         print(f"Actual price: {Fore.YELLOW}{last_price:.2f} ${Style.RESET_ALL}\n")
         print("Prediction:")
@@ -44,12 +49,14 @@ def print_pretty_prediction_nn(df_pred: pd.DataFrame, last_price: float):
         print(df_pred.to_string())
 
 
-def mean_absolute_percentage_error(y_true, y_pred):
+def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> np.number:
+    """Calculate mean absolute percent error"""
     y_true, y_pred = np.array(y_true), np.array(y_pred)
     return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
 
 
-def print_prediction_kpis(real, pred):
+def print_prediction_kpis(real: np.ndarray, pred: np.ndarray):
+    """Print prediction statistics"""
     print("KPIs")
     print(f"MAPE: {mean_absolute_percentage_error(real, pred):.3f} %")
     print(f"R2: {r2_score(real, pred):.3f}")
@@ -59,6 +66,7 @@ def print_prediction_kpis(real, pred):
 
 
 def price_prediction_backtesting_color(val: list) -> str:
+    """Add color to backtest data"""
     err_pct = 100 * (val[0] - val[1]) / val[1]
     if val[0] > val[1]:
         s_err_pct = f"       {Fore.GREEN} +{err_pct:.2f} %"

tests/test_pred_neural_networks.py

@@ -2,7 +2,7 @@
 import unittest
 
 try:
-    from gamestonk_terminal.prediction_techniques.neural_networks import mlp
+    from gamestonk_terminal.prediction_techniques.neural_networks_view import mlp
 except ModuleNotFoundError as e:
     print("One of the optional packages seems to be missing")
     print(e)