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Jun 16, 2025
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refactor: Remove seasonality detection and add comprehensive test script #1

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121 changes: 94 additions & 27 deletions pyautoplot/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,8 @@
import pandas as pd
import numpy as np
import os
import warnings
# from statsmodels.tsa.stattools import acf # Removed as _detect_seasonality is removed

class AutoPlot:
"""
Expand Down Expand Up @@ -47,6 +49,11 @@ def _detect_column_types(self):
elif pd.api.types.is_numeric_dtype(self.data[column]):
self.numeric.append(column)

# Check for unclassified columns
for column in self.data.columns:
if column not in self.numeric and column not in self.categorical and column not in self.time_series:
warnings.warn(f"Warning: Column '{column}' was not classified and will be ignored.")

def _apply_theme(self, theme):
"""Apply theme settings for light, dark, or custom themes."""
if theme == "dark":
Expand Down Expand Up @@ -101,8 +108,8 @@ def _generate_analysis(self):
n = len(data_column) # Get the number of valid data points
mean = data_column.mean()
stddev = data_column.std()
skewness = self._calculate_skewness(self, data=data_column, mean=mean, stddev=stddev, n=n)
kurtosis = self._calculate_kurtosis(self, data=data_column, mean=mean, stddev=stddev, n=n)
skewness = self._calculate_skewness(data=data_column, mean=mean, stddev=stddev, n=n)
kurtosis = self._calculate_kurtosis(data=data_column, mean=mean, stddev=stddev, n=n)

stats = {
"Type": "Numeric",
Expand All @@ -119,7 +126,7 @@ def _generate_analysis(self):
"50th Percentile (Median)": data_column.quantile(0.50),
"75th Percentile": data_column.quantile(0.75),
"Outliers": self._detect_outliers(data_column),
"Missing Values": data_column.isnull().sum(),
"Missing Values": self.data[column].isnull().sum(),
}
analysis[column] = stats

Expand All @@ -136,43 +143,57 @@ def _generate_analysis(self):
"Balance Ratio": self._calculate_balance_ratio(self.data[column]),
"Frequency Distribution": value_counts.to_dict(),
"Mode Variability": value_counts[value_counts == value_counts.max()].index.tolist(),
"Missing Values": data_column.isnull().sum(),
"Missing Values": self.data[column].isnull().sum(),
}
analysis[column] = stats

# Time Series Analysis
for column in self.time_series:
data_column = self.data[column].dropna()

# seasonality_result logic removed
autocorrelation_result = [np.nan] * 10 # Default for autocorrelation

if not data_column.empty:
# seasonality_result = self._detect_seasonality(data_column) # Removed
autocorrelation_result = self._calculate_autocorrelation(data_column)

stats = {
"Type": "Time Series",
"Count": len(data_column),
"Min": data_column.min(),
"Max": data_column.max(),
"Mean": data_column.mean(),
"Median": data_column.median(),
"Missing Values": data_column.isnull().sum(),
"Seasonality": self._detect_seasonality(data_column),
"Autocorrelation": self._calculate_autocorrelation(data_column),
"Count": len(data_column), # This will be 0 if empty
"Min": data_column.min() if not data_column.empty else np.nan,
"Max": data_column.max() if not data_column.empty else np.nan,
"Mean": data_column.mean() if not data_column.empty else np.nan,
"Median": data_column.median() if not data_column.empty else np.nan,
"Missing Values": self.data[column].isnull().sum(),
# "Seasonality": seasonality_result, # Removed
"Autocorrelation": autocorrelation_result,
}
analysis[column] = stats

return analysis

@staticmethod
def _calculate_skewness(self, data, mean, stddev, n):
def _calculate_skewness(data, mean, stddev, n):
"""Calculate skewness of a dataset."""
if stddev == 0 or n == 0:
return np.nan
skewness = np.sum(((data - mean) / stddev) ** 3) / n
return skewness

@staticmethod
def _calculate_kurtosis(self, data, mean, stddev, n):
def _calculate_kurtosis(data, mean, stddev, n):
"""Calculate kurtosis of a dataset."""
if stddev == 0 or n == 0:
return np.nan
kurtosis = (np.sum(((data - mean) / stddev) ** 4) / n) - 3
return kurtosis

@staticmethod
def _calculate_autocorrelation(self, data):
def _calculate_autocorrelation(data):
"""Calculate autocorrelation at various lags."""
if len(data) < 2 or data.var() == 0:
return [np.nan] * 10 # Return list of NaNs for 10 lags
mean = data.mean()
autocorrelations = []
for lag in range(1, 11): # Calculate autocorrelation for lags 1 through 10
Expand All @@ -181,6 +202,7 @@ def _calculate_autocorrelation(self, data):
autocorrelations.append(correlation)
return autocorrelations

# _detect_seasonality method removed

@staticmethod
def _detect_outliers(series, threshold=3):
Expand Down Expand Up @@ -221,15 +243,15 @@ def _plot_detailed_analysis(self, analysis, output_file=None):

if output_file:
plt.savefig(output_file, dpi=300)
plt.show()
# plt.show() will be called from auto_plot

def auto_plot(self, output_file=None, theme="light", excludes=None, **kwargs):
"""
Automatically analyze the dataset and generate visualizations.

This method produces:
1. Detailed analysis summary as a text-based plot.
2. Numeric visualizations: Histograms, Boxplots, and Pairwise Scatter Matrix.
2. Numeric visualizations: Histograms, Boxplots, and Pairwise Scatter Matrix. Note: Pairwise Scatter Matrix can be resource-intensive for datasets with many numeric columns. Consider using the `excludes=['pairwise_scatter']` option for large datasets.
3. Categorical visualizations: Enhanced Bar Plots and Pie Charts.
4. Time-series visualizations: Line and Stacked Area Plots.

Expand All @@ -256,7 +278,9 @@ def auto_plot(self, output_file=None, theme="light", excludes=None, **kwargs):
# Section 1: Detailed Analysis Summary
if "detailed_analysis" not in excludes:
analysis = self._generate_analysis()
self._plot_detailed_analysis(analysis, output_file=f"{base_filename}_analysis{file_extension}")
self._plot_detailed_analysis(analysis, output_file=f"{base_filename}_analysis{file_extension}" if output_file else None)
plt.show()
plt.close('all')

# Section 2: Numeric Distributions and Boxplots
if self.numeric and "numeric" not in excludes:
Expand All @@ -278,6 +302,8 @@ def auto_plot(self, output_file=None, theme="light", excludes=None, **kwargs):
if output_file:
# Save Numeric section plots
plt.savefig(f"{base_filename}_numeric{file_extension}", dpi=kwargs.get("dpi", 300))
plt.show()
plt.close('all')

# Section 3: Enhanced Bar Plots for Categorical Variables
if self.categorical and "categorical" not in excludes:
Expand Down Expand Up @@ -308,6 +334,8 @@ def auto_plot(self, output_file=None, theme="light", excludes=None, **kwargs):
if output_file:
# Save Categorical section plots
plt.savefig(f"{base_filename}_categorical{file_extension}", dpi=kwargs.get("dpi", 300))
plt.show()
plt.close('all')

# Section 4: Pairwise Scatter Plots for Numeric Variables
if len(self.numeric) > 1 and "pairwise_scatter" not in excludes:
Expand All @@ -329,6 +357,8 @@ def auto_plot(self, output_file=None, theme="light", excludes=None, **kwargs):
if output_file:
# Save Pairwise section plots
plt.savefig(f"{base_filename}_pairwise{file_extension}", dpi=kwargs.get("dpi", 300))
plt.show()
plt.close('all')

# Section 5: Pie Charts for Categorical Variables
if self.categorical and "pie_charts" not in excludes:
Expand All @@ -342,6 +372,8 @@ def auto_plot(self, output_file=None, theme="light", excludes=None, **kwargs):
if output_file:
# Save Pie section plots
plt.savefig(f"{base_filename}_pie_{cat_column}{file_extension}", dpi=kwargs.get("dpi", 300))
plt.show()
plt.close('all')

# Section 6: Line Plots for Time Series Data
if self.time_series and "line_plots" not in excludes: # Assuming self.time_series contains time series columns
Expand All @@ -354,6 +386,8 @@ def auto_plot(self, output_file=None, theme="light", excludes=None, **kwargs):
if output_file:
# Save Line plot section
plt.savefig(f"{base_filename}_line_{time_col}{file_extension}", dpi=kwargs.get("dpi", 300))
plt.show()
plt.close('all')

# Section 7: Stacked Area Plots for Time Series Data
if self.time_series and "stacked_area" not in excludes: # If self.time_series contains multiple time series columns for stacking
Expand All @@ -365,9 +399,10 @@ def auto_plot(self, output_file=None, theme="light", excludes=None, **kwargs):
if output_file:
# Save Stacked Area section plot
plt.savefig(f"{base_filename}_stacked_area{file_extension}", dpi=kwargs.get("dpi", 300))

# Show all generated plots
plt.show()
plt.close('all')

# No final plt.show() here anymore

def plot(self, plot_type, x=None, y=None, **kwargs):
"""
Expand All @@ -387,14 +422,46 @@ def plot(self, plot_type, x=None, y=None, **kwargs):
autoplot.plot(plot_type="scatter", x="age", y="income")
autoplot.plot(plot_type="distribution", x="salary", bins=20)
"""
if plot_type == "scatter" and x and y:
if plot_type == "scatter":
if x is None:
raise ValueError("Argument 'x' is required for scatter plot.")
if y is None:
raise ValueError("Argument 'y' is required for scatter plot.")
if x not in self.data.columns:
raise ValueError(f"Column '{x}' not found in dataset.")
if y not in self.data.columns:
raise ValueError(f"Column '{y}' not found in dataset.")
self.data.plot.scatter(x=x, y=y, **kwargs)
elif plot_type == "distribution" and x:
self.data[x].plot(kind="hist", **kwargs)
elif plot_type == "boxplot" and x:
self.data.boxplot(column=x, **kwargs)
elif plot_type == "bar" and x:
self.data[x].value_counts().plot(kind="bar", **kwargs)
elif plot_type in ["distribution", "boxplot", "bar"]:
if x is None:
raise ValueError("Argument 'x' is required for this plot type.")
if x not in self.data.columns:
raise ValueError(f"Column '{x}' not found in dataset.")

if plot_type == "distribution":
self.data[x].plot(kind="hist", **kwargs)
elif plot_type == "boxplot":
self.data.boxplot(column=x, **kwargs)
elif plot_type == "bar":
self.data[x].value_counts().plot(kind="bar", **kwargs)
else:
# Handling other plot types or raising an error for unsupported ones
# For now, let's assume if it's not scatter, distribution, boxplot or bar, it's an issue or needs specific handling
# This part can be expanded based on future plot types.
# If x is generally required for other plots, this check can be more generic.
if x is None: # A more generic check if x is usually required
raise ValueError("Argument 'x' is required for this plot type.")
# If x is provided, but plot_type is unknown
if x is not None and x not in self.data.columns:
raise ValueError(f"Column '{x}' not found in dataset.")
# Defaulting to a simple plot or error if plot_type is not recognized
# For now, just showing plot, which might lead to errors if not configured correctly.
# Consider raising ValueError for unknown plot_type for robustness.
# For example: raise ValueError(f"Plot type '{plot_type}' is not supported.")
# However, current structure seems to rely on specific plot_type checks above.
else:
raise ValueError(f"Plot type '{plot_type}' is not supported or invalid arguments provided.")

plt.show()

def customize(self, **kwargs):
Expand Down
89 changes: 83 additions & 6 deletions test.py
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Open in desktop
Original file line number Diff line number Diff line change
@@ -1,10 +1,87 @@
import os
import shutil
from pyautoplot import AutoPlot
import pandas as pd
import matplotlib.pyplot as plt

# Initialize with a CSV file
plotter = AutoPlot("energy_consumption_dataset.csv")
OUTPUT_DIR = "test_output"
DATASET_PATH = "energy_consumption_dataset.csv"

# Automatically analyze and plot
plotter.auto_plot(output_file='test', theme="dark", color='orange', excludes=['detailed_analysis'])
if os.path.exists(OUTPUT_DIR):
shutil.rmtree(OUTPUT_DIR)
os.makedirs(OUTPUT_DIR, exist_ok=True)

# Manually plot data
plotter.plot(plot_type="scatter", x="Month", y="Hour")
print(f"PyAutoPlot Test. Output: {os.path.abspath(OUTPUT_DIR)}")

if not os.path.exists(DATASET_PATH):
print(f"Dummy data created for {DATASET_PATH}")
dummy_data = {
'Timestamp': pd.to_datetime([f'2023-01-01 {h:02}:00' for h in range(24)] * 3), # 3 days
'Temperature': [10+i*0.1 for i in range(24*3)],
'EnergyConsumption': [100+i for i in range(24*3)],
'DayOfWeek': (['Mon']*24 + ['Tue']*24 + ['Wed']*24),
'Category': (['A','B','C']*24)
}
pd.DataFrame(dummy_data).to_csv(DATASET_PATH, index=False)

try:
plotter = AutoPlot(DATASET_PATH)
print("AutoPlot initialized.")
except Exception as e:
print(f"Init Error: {e}"); exit()

print("Testing auto_plot()...")
try:
plotter.auto_plot(output_file=os.path.join(OUTPUT_DIR, "ap_default.png"))
except Exception as e: print(f"Error default auto_plot: {e}")

try:
plotter.auto_plot(output_file=os.path.join(OUTPUT_DIR, "ap_custom.png"), theme="dark", excludes=['pairwise_scatter'])
except Exception as e: print(f"Error custom auto_plot: {e}")

print("Testing manual plot()...")
n1 = plotter.numeric[0] if plotter.numeric else None
n2 = plotter.numeric[1] if len(plotter.numeric) > 1 else n1
c1 = plotter.categorical[0] if plotter.categorical else None

if n1 and n2:
try:
plotter.plot(plot_type="scatter", x=n1, y=n2, title="Scatter")
plt.savefig(os.path.join(OUTPUT_DIR, "m_scatter.png")); plt.show(); plt.close('all')
except Exception as e: print(f"Error scatter: {e}")
if n1:
try:
plotter.plot(plot_type="distribution", x=n1, title="Dist", bins=10)
plt.savefig(os.path.join(OUTPUT_DIR, "m_dist.png")); plt.show(); plt.close('all')
except Exception as e: print(f"Error distribution: {e}")
try:
plotter.plot(plot_type="boxplot", x=n1, title="Box")
plt.savefig(os.path.join(OUTPUT_DIR, "m_box.png")); plt.show(); plt.close('all')
except Exception as e: print(f"Error boxplot: {e}")
if c1:
try:
plotter.plot(plot_type="bar", x=c1, title="Bar")
plt.savefig(os.path.join(OUTPUT_DIR, "m_bar.png")); plt.show(); plt.close('all')
except Exception as e: print(f"Error bar: {e}")

print("Testing customize()...")
if n1:
try:
plotter.customize(**{"font.size": 8, "figure.facecolor": "lightyellow"})
plotter.plot(plot_type="distribution", x=n1, title="Custom Dist")
plt.savefig(os.path.join(OUTPUT_DIR, "m_custom_dist.png")); plt.show(); plt.close('all')
import matplotlib
plotter.customize(**matplotlib.rcParamsDefault) # Revert
except Exception as e: print(f"Error customize: {e}")

print("Testing small dataset...")
try:
small_data = {'A': [1,2,3], 'B': ['x','y','z'], 'C': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03'])}
small_df_path = os.path.join(OUTPUT_DIR, "small_ds.csv")
pd.DataFrame(small_data).to_csv(small_df_path, index=False)
small_plotter = AutoPlot(csv_path=small_df_path)
small_plotter.auto_plot(output_file=os.path.join(OUTPUT_DIR, "ap_small_ds.png"))
except Exception as e: print(f"Error small dataset: {e}")

print("Test Script Finished.")
```