updates

metricdata/graph.py

@@ -0,0 +1,44 @@
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Function to create line graph and save it for each file
+def create_line_graph(file_path):
+    # Read CSV file
+    df = pd.read_csv(file_path, parse_dates=['Date'])
+
+    # Extract data
+    date = df['Date']
+    prediction = df['Prediction']
+    actual = df['Actual']
+
+    # Plot line graph
+    plt.figure(figsize=(10, 6))
+    plt.plot(date, prediction, label='Prediction', marker='o')
+    plt.plot(date, actual, label='Actual', marker='o')
+
+    # Add title and labels
+    file_name = os.path.splitext(os.path.basename(file_path))[0]
+    plt.title(f'{file_name} - Prediction vs Actual')
+    plt.xlabel('Date')
+    plt.ylabel('Value')
+
+    # Rotate x-axis labels for better readability
+    plt.xticks(rotation=45)
+
+    # Add legend
+    plt.legend()
+
+    # Save graph as image file
+    file_name = file_name + '_line_graph.png'
+    plt.savefig(file_name, bbox_inches='tight')  # Adjusted to fit the date in the picture
+    plt.close()
+
+# Directory path containing CSV files
+directory_path = 'metricdata'
+
+# Iterate over files in directory
+for file in os.listdir(directory_path):
+    if file.endswith('.csv'):
+        file_path = os.path.join(directory_path, file)
+        create_line_graph(file_path)

metdata/performance_metrics.py → metricdata/performance_metrics.py

@@ -1,15 +1,22 @@
+# Import necessary libraries
 import pandas as pd
 import numpy as np
 import os
 from math import sqrt
 from sklearn.metrics import r2_score, explained_variance_score as ev_score
 import matplotlib.pyplot as plt
 
+# Function to calculate performance metrics
 def calculate_metrics(data):
+    # Define column names for actual and predicted values
     actual_col = 'Actual'
     predicted_col = 'Prediction'
+
+    # Extract actual and predicted values from the data
     actual = data[actual_col]
     predicted = data[predicted_col]
+
+    # Calculate various performance metrics
     metrics = {}
     metrics['MAE'] = mean_absolute_error(actual, predicted)
     metrics['RMSE'] = root_mean_squared_error(actual, predicted)
@@ -18,51 +25,69 @@ def calculate_metrics(data):
     metrics['Median_Absolute_Error'] = median_absolute_error(actual, predicted)
     return metrics
 
+# Function to calculate Mean Absolute Error (MAE)
 def mean_absolute_error(actual, predicted):
     return np.mean(np.abs(actual - predicted))
 
+# Function to calculate Root Mean Squared Error (RMSE)
 def root_mean_squared_error(actual, predicted):
     return sqrt(mean_squared_error(actual, predicted))
 
+# Function to calculate R-squared value
 def r_squared(actual, predicted):
     return r2_score(actual, predicted)
 
+# Function to calculate Median Absolute Error
 def median_absolute_error(actual, predicted):
     return np.median(np.abs(actual - predicted))
 
+# Function to calculate Mean Squared Error (MSE)
 def mean_squared_error(actual, predicted):
     return np.mean((actual - predicted) ** 2)
 
+# Function to load data from CSV files in a directory
 def load_data(data_dir):
     data_frames = {}
     for file in os.listdir(data_dir):
         if file.endswith('.csv'):
+            # Extract ticker symbol from filename
             ticker = os.path.splitext(file)[0]
+            # Read CSV file and store in dictionary with ticker symbol as key
             data_frames[ticker] = pd.read_csv(os.path.join(data_dir, file))
     return data_frames
 
+# Function to analyze performance metrics for all files in a directory
 def analyze_all_files(directory_path):
+    # Load data from CSV files
     data_frames = load_data(directory_path)
     metrics_results = {}
+    # Calculate metrics for each ticker
     for ticker, df in data_frames.items():
         metrics = calculate_metrics(df)
         metrics_results[ticker] = metrics
     return metrics_results
 
+# Function to plot performance metrics for each ticker
 def plot_metrics(metrics_results):
+    # Convert metrics results to DataFrame
     metrics_df = pd.DataFrame(metrics_results).T
+    # Plot bar graph
     metrics_df.plot(kind='bar', figsize=(12, 8))
+    # Add title and labels
     plt.title('Performance Metrics for Each Ticker')
     plt.xlabel('Ticker')
     plt.ylabel('Metric Value')
     plt.xticks(rotation=45)
+    # Show plot
     plt.legend(loc='upper left', bbox_to_anchor=(1.0, 1.0))
     plt.tight_layout()
     plt.show()
 
-# Example usage
-directory_path = 'metdata'
+# Directory path containing CSV files
+directory_path = 'metricdata'
+# Analyze performance metrics for all files in the directory
 all_metrics = analyze_all_files(directory_path)
+# Print metrics results
 print(all_metrics)
-
+# Plot performance metrics
 plot_metrics(all_metrics)