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import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import statsmodels.api as sm
from sklearn.metrics import mean_absolute_error, r2_score,mean_absolute_percentage_error
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from statsmodels.stats.outliers_influence import variance_inflation_factor
from plotly.subplots import make_subplots
st.set_option('deprecation.showPyplotGlobalUse', False)
from datetime import datetime
import seaborn as sns
def plot_actual_vs_predicted(date, y, predicted_values, model, target_column=None, flag=None, repeat_all_years=False, is_panel=False):
"""
Plots actual vs predicted values with optional flags and aggregation for panel data.
Parameters:
date (pd.Series): Series of dates for x-axis.
y (pd.Series): Actual values.
predicted_values (pd.Series): Predicted values from the model.
model (object): Trained model object.
target_column (str, optional): Name of the target column.
flag (tuple, optional): Start and end dates for flagging periods.
repeat_all_years (bool, optional): Whether to repeat flags for all years.
is_panel (bool, optional): Whether the data is panel data requiring aggregation.
Returns:
metrics_table (pd.DataFrame): DataFrame containing MAPE, R-squared, and Adjusted R-squared.
line_values (list): List of flag values for plotting.
fig (go.Figure): Plotly figure object.
"""
if flag is not None:
fig = make_subplots(specs=[[{"secondary_y": True}]])
else:
fig = go.Figure()
if is_panel:
df = pd.DataFrame()
df['date'] = date
df['Actual'] = y
df['Predicted'] = predicted_values
df_agg = df.groupby('date').agg({'Actual': 'sum', 'Predicted': 'sum'}).reset_index()
df_agg.columns = ['date', 'Actual', 'Predicted']
assert len(df_agg) == pd.Series(date).nunique()
fig.add_trace(go.Scatter(x=df_agg['date'], y=df_agg['Actual'], mode='lines', name='Actual', line=dict(color='#08083B')))
fig.add_trace(go.Scatter(x=df_agg['date'], y=df_agg['Predicted'], mode='lines', name='Predicted', line=dict(color='#11B6BD')))
else:
fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='#08083B')))
fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='#11B6BD')))
line_values = []
if flag:
min_date, max_date = flag[0], flag[1]
min_week = datetime.strptime(str(min_date), "%Y-%m-%d").strftime("%U")
max_week = datetime.strptime(str(max_date), "%Y-%m-%d").strftime("%U")
if repeat_all_years:
line_values = list(pd.Series(date).map(lambda x: 1 if (pd.Timestamp(x).week >= int(min_week)) & (pd.Timestamp(x).week <= int(max_week)) else 0))
assert len(line_values) == len(date)
fig.add_trace(go.Scatter(x=date, y=line_values, mode='lines', name='Flag', line=dict(color='#FF5733')), secondary_y=True)
else:
line_values = list(pd.Series(date).map(lambda x: 1 if (pd.Timestamp(x) >= pd.Timestamp(min_date)) and (pd.Timestamp(x) <= pd.Timestamp(max_date)) else 0))
fig.add_trace(go.Scatter(x=date, y=line_values, mode='lines', name='Flag', line=dict(color='#FF5733')), secondary_y=True)
mape = mean_absolute_percentage_error(y, predicted_values)
r2 = r2_score(y, predicted_values)
adjr2 = 1 - (1 - r2) * (len(y) - 1) / (len(y) - len(model.params) - 1)
metrics_table = pd.DataFrame({
'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
'Value': [mape, r2, adjr2]
})
fig.update_layout(
xaxis=dict(title='Date'),
yaxis=dict(title=target_column),
xaxis_tickangle=-30
)
fig.add_annotation(
text=f"MAPE: {mape * 100:0.1f}%, Adj. R-squared: {adjr2 * 100:.1f}%",
xref="paper",
yref="paper",
x=0.95,
y=1.2,
showarrow=False,
)
return metrics_table, line_values, fig
def plot_residual_predicted(actual, predicted, df):
"""
Plots standardized residuals against predicted values.
Parameters:
actual (pd.Series): Actual values.
predicted (pd.Series): Predicted values.
df (pd.DataFrame): DataFrame containing the data.
Returns:
fig (go.Figure): Plotly figure object.
"""
df_ = df.copy()
df_['Residuals'] = actual - pd.Series(predicted)
df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5, color_discrete_sequence=["#11B6BD"])
fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
fig.add_hline(y=2, line_color="red")
fig.add_hline(y=-2, line_color="red")
fig.update_xaxes(title='Predicted')
fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
fig.update_layout(title='2.3.1 Residuals over Predicted Values', autosize=False, width=600, height=400)
return fig
def residual_distribution(actual, predicted):
"""
Plots the distribution of residuals.
Parameters:
actual (pd.Series): Actual values.
predicted (pd.Series): Predicted values.
Returns:
plt (matplotlib.pyplot): Matplotlib plot object.
"""
Residuals = actual - pd.Series(predicted)
sns.set(style="whitegrid")
plt.figure(figsize=(6, 4))
sns.histplot(Residuals, kde=True, color="#11B6BD")
plt.title('2.3.3 Distribution of Residuals')
plt.xlabel('Residuals')
plt.ylabel('Probability Density')
return plt
def qqplot(actual, predicted):
"""
Creates a QQ plot of the residuals.
Parameters:
actual (pd.Series): Actual values.
predicted (pd.Series): Predicted values.
Returns:
fig (go.Figure): Plotly figure object.
"""
Residuals = actual - pd.Series(predicted)
Residuals = pd.Series(Residuals)
Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
fig = go.Figure()
fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
y=sm.ProbPlot(Resud_std).sample_quantiles,
mode='markers',
marker=dict(size=5, color="#11B6BD"),
name='QQ Plot'))
diagonal_line = go.Scatter(
x=[-2, 2],
y=[-2, 2],
mode='lines',
line=dict(color='red'),
name=' '
)
fig.add_trace(diagonal_line)
fig.update_layout(title='2.3.2 QQ Plot of Residuals', title_x=0.5, autosize=False, width=600, height=400,
xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
return fig
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