Improve pred files, and non-blocking plot

gamestonk_terminal/prediction_techniques/arima.py

@@ -4,11 +4,15 @@ import pandas as pd
 from pandas.plotting import register_matplotlib_converters
 import pmdarima
 from statsmodels.tsa.arima.model import ARIMA
-from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
+from gamestonk_terminal.helper_funcs import (
+    check_positive,
+    get_next_stock_market_days,
+    parse_known_args_and_warn,
+)
+
 
 register_matplotlib_converters()
 
-# -------------------------------------------------- ARIMA --------------------------------------------------
 # pylint: disable=unused-argument
 def arima(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
@@ -71,11 +75,7 @@ def arima(l_args, s_ticker, s_interval, df_stock):
     )
 
     try:
-        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
-
-        if l_unknown_args:
-            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
-            return
+        ns_parser = parse_known_args_and_warn(parser, l_args)
 
         # Machine Learning model
         if ns_parser.s_order:
@@ -115,6 +115,7 @@ def arima(l_args, s_ticker, s_interval, df_stock):
             print("")
 
         # Plotting
+        plt.figure()
         plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
         if ns_parser.s_order:
             plt.title(
@@ -147,6 +148,7 @@ def arima(l_args, s_ticker, s_interval, df_stock):
         plt.vlines(
             df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k"
         )
+        plt.ion()
         plt.show()
 
         # Print prediction data

gamestonk_terminal/prediction_techniques/fbprophet.py

@@ -4,13 +4,16 @@ import matplotlib.pyplot as plt
 import pandas as pd
 from pandas.plotting import register_matplotlib_converters
 from fbprophet import Prophet
-from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
+from gamestonk_terminal.helper_funcs import (
+    check_positive,
+    get_next_stock_market_days,
+    parse_known_args_and_warn,
+)
 
 register_matplotlib_converters()
 
 warnings.simplefilter("ignore")
 
-# ----------------------------------------------------- FBPROPHET -----------------------------------------------------
 # pylint: disable=unused-argument
 def fbprophet(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
@@ -33,11 +36,7 @@ def fbprophet(l_args, s_ticker, s_interval, df_stock):
     )
 
     try:
-        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
-
-        if l_unknown_args:
-            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
-            return
+        ns_parser = parse_known_args_and_warn(parser, l_args)
 
         df_stock = df_stock.sort_index(ascending=True)
         df_stock.reset_index(level=0, inplace=True)
@@ -77,6 +76,7 @@ def fbprophet(l_args, s_ticker, s_interval, df_stock):
             df_stock["ds"].values[0], get_next_stock_market_days(l_pred_days[-1], 1)[-1]
         )
         plt.title(f"Fb Prophet on {s_ticker} - {ns_parser.n_days} days prediction")
+        plt.ion()
         plt.show()
 
         print("")

gamestonk_terminal/prediction_techniques/knn.py

@@ -4,11 +4,15 @@ import pandas as pd
 from pandas.plotting import register_matplotlib_converters
 from sklearn import neighbors
 from TimeSeriesCrossValidation import splitTrain
-from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
+from gamestonk_terminal.helper_funcs import (
+    check_positive,
+    get_next_stock_market_days,
+    parse_known_args_and_warn,
+)
 
 register_matplotlib_converters()
 
-# ----------------------------------------------------- kNN -----------------------------------------------------
+
 # pylint: disable=unused-argument
 def k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
@@ -58,11 +62,7 @@ def k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock):
     )
 
     try:
-        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
-
-        if l_unknown_args:
-            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
-            return
+        ns_parser = parse_known_args_and_warn(parser, l_args)
 
         # Split training data
         stock_x, stock_y = splitTrain.split_train(
@@ -87,6 +87,7 @@ def k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock):
         df_pred = pd.Series(l_predictions, index=l_pred_days, name="Price")
 
         # Plotting
+        plt.figure()
         plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
         plt.title(
             f"{ns_parser.n_neighbors}-Nearest Neighbors on {s_ticker} - {ns_parser.n_days} days prediction"
@@ -114,6 +115,7 @@ def k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock):
         plt.vlines(
             df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k"
         )
+        plt.ion()
         plt.show()
 
         # Print prediction data

gamestonk_terminal/prediction_techniques/neural_networks.py

@@ -18,7 +18,12 @@ from tensorflow.keras.layers import (
     # TimeDistributed,
 )
 
-from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
+from gamestonk_terminal.helper_funcs import (
+    check_positive,
+    get_next_stock_market_days,
+    parse_known_args_and_warn,
+)
+
 from gamestonk_terminal import config_neural_network_models as cfg_nn_models
 
 
@@ -77,7 +82,6 @@ def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
     return model
 
 
-# -------------------------------------------------- MLP --------------------------------------------------
 # pylint: disable=unused-argument
 def mlp(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
@@ -159,11 +163,7 @@ def mlp(l_args, s_ticker, s_interval, df_stock):
     )
 
     try:
-        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
-
-        if l_unknown_args:
-            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
-            return
+        ns_parser = parse_known_args_and_warn(parser, l_args)
 
         # Pre-process data
         if ns_parser.s_preprocessing == "standardization":
@@ -227,6 +227,7 @@ def mlp(l_args, s_ticker, s_interval, df_stock):
         df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
 
         # Plotting
+        plt.figure()
         plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
         plt.title(f"MLP on {s_ticker} - {ns_parser.n_days} days prediction")
         plt.xlim(
@@ -258,6 +259,7 @@ def mlp(l_args, s_ticker, s_interval, df_stock):
             linestyle="--",
             color="k",
         )
+        plt.ion()
         plt.show()
 
         # Print prediction data
@@ -271,7 +273,6 @@ def mlp(l_args, s_ticker, s_interval, df_stock):
         print("")
 
 
-# -------------------------------------------------- RNN --------------------------------------------------
 # pylint: disable=unused-argument
 def rnn(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
@@ -353,11 +354,7 @@ def rnn(l_args, s_ticker, s_interval, df_stock):
     )
 
     try:
-        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
-
-        if l_unknown_args:
-            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
-            return
+        ns_parser = parse_known_args_and_warn(parser, l_args)
 
         # Pre-process data
         if ns_parser.s_preprocessing == "standardization":
@@ -421,6 +418,7 @@ def rnn(l_args, s_ticker, s_interval, df_stock):
         df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
 
         # Plotting
+        plt.figure()
         plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
         plt.title(f"RNN on {s_ticker} - {ns_parser.n_days} days prediction")
         plt.xlim(
@@ -452,6 +450,7 @@ def rnn(l_args, s_ticker, s_interval, df_stock):
             linestyle="--",
             color="k",
         )
+        plt.ion()
         plt.show()
 
         # Print prediction data
@@ -465,7 +464,6 @@ def rnn(l_args, s_ticker, s_interval, df_stock):
         print("")
 
 
-# -------------------------------------------------- LSTM --------------------------------------------------
 # pylint: disable=unused-argument
 def lstm(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
@@ -547,11 +545,7 @@ def lstm(l_args, s_ticker, s_interval, df_stock):
     )
 
     try:
-        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
-
-        if l_unknown_args:
-            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
-            return
+        ns_parser = parse_known_args_and_warn(parser, l_args)
 
         # Pre-process data
         if ns_parser.s_preprocessing == "standardization":
@@ -615,6 +609,7 @@ def lstm(l_args, s_ticker, s_interval, df_stock):
         df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
 
         # Plotting
+        plt.figure()
         plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
         plt.title(f"LSTM on {s_ticker} - {ns_parser.n_days} days prediction")
         plt.xlim(
@@ -646,6 +641,7 @@ def lstm(l_args, s_ticker, s_interval, df_stock):
             linestyle="--",
             color="k",
         )
+        plt.ion()
         plt.show()
 
         # Print prediction data

gamestonk_terminal/prediction_techniques/pred_menu.py

@@ -6,8 +6,9 @@ from gamestonk_terminal.prediction_techniques import regression
 from gamestonk_terminal.prediction_techniques import arima
 from gamestonk_terminal.prediction_techniques import fbprophet
 from gamestonk_terminal.prediction_techniques import neural_networks
+import matplotlib.pyplot as plt
+
 
-# -----------------------------------------------------------------------------------------------------------------------
 def print_prediction(s_ticker, s_start, s_interval):
     """ Print help """
 
@@ -69,6 +70,9 @@ def pred_menu(df_stock, s_ticker, s_start, s_interval):
         # Get input command from user
         as_input = input("> ")
 
+        # Images are non blocking - allows to close them if we type other command
+        plt.close()
+
         # Parse prediction techniques command of the list of possible commands
         try:
             (ns_known_args, l_args) = pred_parser.parse_known_args(as_input.split())

gamestonk_terminal/prediction_techniques/regression.py

@@ -7,7 +7,11 @@ from sklearn import linear_model
 from sklearn import pipeline
 from sklearn import preprocessing
 
-from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
+from gamestonk_terminal.helper_funcs import (
+    check_positive,
+    get_next_stock_market_days,
+    parse_known_args_and_warn,
+)
 
 register_matplotlib_converters()
 
@@ -16,7 +20,6 @@ LINEAR = 1
 QUADRATIC = 2
 CUBIC = 3
 
-# -------------------------------------------------- REGRESSION --------------------------------------------------
 # pylint: disable=unused-argument
 def regression(l_args, s_ticker, s_interval, df_stock, polynomial):
     parser = argparse.ArgumentParser(
@@ -69,11 +72,7 @@ def regression(l_args, s_ticker, s_interval, df_stock, polynomial):
         )
 
     try:
-        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
-
-        if l_unknown_args:
-            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
-            return
+        ns_parser = parse_known_args_and_warn(parser, l_args)
 
         # Split training data
         stock_x, stock_y = splitTrain.split_train(
@@ -106,6 +105,7 @@ def regression(l_args, s_ticker, s_interval, df_stock, polynomial):
         df_pred = pd.Series(l_predictions, index=l_pred_days, name="Price")
 
         # Plotting
+        plt.figure()
         plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
         plt.title(
             f"Regression (polynomial {polynomial}) on {s_ticker} - {ns_parser.n_days} days prediction"
@@ -133,6 +133,7 @@ def regression(l_args, s_ticker, s_interval, df_stock, polynomial):
         plt.vlines(
             df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k"
         )
+        plt.ion()
         plt.show()
 
         # Print prediction data

gamestonk_terminal/prediction_techniques/sma.py

@@ -4,11 +4,14 @@ import matplotlib.pyplot as plt
 import pandas as pd
 from pandas.plotting import register_matplotlib_converters
 
-from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
+from gamestonk_terminal.helper_funcs import (
+    check_positive,
+    get_next_stock_market_days,
+    parse_known_args_and_warn,
+)
 
 register_matplotlib_converters()
 
-# ----------------------------------------------------- SMA -----------------------------------------------------
 # pylint: disable=unused-argument
 def simple_moving_average(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
@@ -44,11 +47,7 @@ def simple_moving_average(l_args, s_ticker, s_interval, df_stock):
     )
 
     try:
-        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
-
-        if l_unknown_args:
-            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
-            return
+        ns_parser = parse_known_args_and_warn(parser, l_args)
 
         # Prediction data
         l_predictions = list()
@@ -97,6 +96,7 @@ def simple_moving_average(l_args, s_ticker, s_interval, df_stock):
         plt.vlines(
             df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k"
         )
+        plt.ion()
         plt.show()
 
         # Print prediction data