Skip to content

PDF

jarvais.utils.pdf

generate_analysis_report_pdf(outlier_analysis, multiplots, categorical_columns, continuous_columns, output_dir)

Generate a PDF report for the analysis, including plots, tables, and outlier analysis.

Parameters:

Name Type Description Default
outlier_analysis str

Text summary of outlier analysis to include in the report.

 required
multiplots list

A list of paths to plots to include in the multiplots section.

 required
categorical_columns list

A list of categorical columns to use for multiplots.

 required
continuous_columns list

A list of continuous columns to use for multiplots.

 required
output_dir str | Path

The directory where the generated PDF report will be saved.

 required

Returns:

Name Type Description
None None

The function saves the generated PDF to the specified output directory.
Source code in src/jarvais/utils/pdf.py 
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
def generate_analysis_report_pdf(
        outlier_analysis: str,
        multiplots: list,
        categorical_columns: list,
        continuous_columns: list,
        output_dir: str | Path
    ) -> None:
    """
    Generate a PDF report for the analysis, including plots, tables, and outlier analysis.

    Args:
        outlier_analysis (str): Text summary of outlier analysis to include in the report.
        multiplots (list): A list of paths to plots to include in the multiplots section.
        categorical_columns (list): A list of categorical columns to use for multiplots.
        continuous_columns (list): A list of continuous columns to use for multiplots.
        output_dir (str | Path): The directory where the generated PDF report will be saved.

    Returns:
        None: The function saves the generated PDF to the specified output directory.
    """
    output_dir = Path(output_dir)
    figures_dir = output_dir / 'figures'

    # Instantiate PDF
    pdf = FPDF()
    pdf.add_page()
    script_dir = Path(__file__).resolve().parent

    # Adding unicode fonts
    font_path = (script_dir / 'fonts/Inter_28pt-Regular.ttf')
    pdf.add_font("inter", style="", fname=font_path)
    font_path = (script_dir / 'fonts/Inter_28pt-Bold.ttf')
    pdf.add_font("inter", style="b", fname=font_path)

    # Title
    pdf.set_font('inter', 'B', 36)
    pdf.cell(text="jarvAIs Analyzer Report\n\n", new_y=YPos.NEXT) 

    # Add outlier analysis
    if outlier_analysis != '':
        pdf.set_y(pdf.get_y() + 10)
        pdf.set_font('inter', 'B', 24)
        pdf.cell(text="Outlier Analysis", new_y=YPos.NEXT)
        pdf = _add_outlier_analysis(pdf, outlier_analysis)

    # Add page-wide pairplots
    if (figures_dir / 'pairplot.png').exists():
        pdf.add_page()
        pdf.set_font('inter', 'B', 24)
        pdf.cell(h=pdf.t_margin, text='Pair Plot of Continuous Variables', new_y=YPos.NEXT)
        img = pdf.image((figures_dir / 'pairplot.png'), Align.C, h=pdf.eph*.5)
        pdf.set_y(pdf.t_margin + img.rendered_height + 25)

    # Add correlation plots
    if (figures_dir / 'pearson_correlation.png').exists() and (figures_dir / 'spearman_correlation.png').exists():
        pdf.set_font('inter', 'B', 24)
        pdf.cell(h=pdf.t_margin, text='Pearson and Spearman Correlation Plots', new_y=YPos.NEXT)

        corr_y = pdf.get_y() + 5

        pdf.image((figures_dir / 'pearson_correlation.png'), Align.L, corr_y, w=pdf.epw*.475)
        pdf.image((figures_dir / 'spearman_correlation.png'), Align.R, corr_y, w=pdf.epw*.475)

    # Add multiplots
    if multiplots and categorical_columns:
        pdf = _add_multiplots(pdf, multiplots, categorical_columns, continuous_columns)

    # Add demographic breakdown "table one"
    path_tableone = output_dir / 'tableone.csv'
    if path_tableone.exists():
        try:
            csv_df = pd.read_csv(path_tableone, na_filter=False).astype(str)
            pdf = _add_tableone(pdf, csv_df)
        except Exception as e:
            logger.warning(f"Unable to add table one to analysis report: {e}")

    # Save PDF
    pdf.output(output_dir / 'analysis_report.pdf')

generate_explainer_report_pdf(problem_type, output_dir)

Generate a PDF report for the explainer with visualizations and metrics.

This function creates a PDF report that includes plots and metrics relevant to the specified problem type. The report is saved in the specified output directory.

Parameters:

Name Type Description Default
problem_type str

The type of machine learning problem. Supported values are 'binary', 'multiclass', 'regression', and 'survival'.

 required
output_dir str | Path

The directory where the generated PDF report will be saved.

 required

Returns:

Name Type Description
None None

The function saves the generated PDF to the specified output directory.
Source code in src/jarvais/utils/pdf.py 
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
def generate_explainer_report_pdf(
        problem_type: str,
        output_dir: str | Path
    ) -> None:
    """
    Generate a PDF report for the explainer with visualizations and metrics.

    This function creates a PDF report that includes plots and metrics 
    relevant to the specified problem type. The report is saved in the 
    specified output directory.

    Args:
        problem_type (str): The type of machine learning problem. 
            Supported values are 'binary', 'multiclass', 'regression', 
            and 'survival'.
        output_dir (str | Path): The directory where the generated PDF 
            report will be saved.

    Returns:
        None: The function saves the generated PDF to the specified output directory.
    """
    output_dir = Path(output_dir)
    figures_dir = output_dir / 'figures'

    # Instantiate PDF
    pdf = FPDF()
    pdf.add_page()
    script_dir = Path(__file__).resolve().parent

    # Adding unicode fonts
    font_path = (script_dir / 'fonts/Inter_28pt-Regular.ttf')
    pdf.add_font("inter", style="", fname=font_path)
    font_path = (script_dir / 'fonts/Inter_28pt-Bold.ttf')
    pdf.add_font("inter", style="b", fname=font_path)
    pdf.set_font('inter', '', 24)

    # Title
    pdf.write(5, "Explainer Report\n\n")

    pdf.image((figures_dir / 'test_metrics_bootstrap.png'), Align.C, h=pdf.eph//3.5, w=pdf.epw-20)
    pdf.image((figures_dir / 'validation_metrics_bootstrap.png'), Align.C, h=pdf.eph//3.5, w=pdf.epw-20)
    pdf.image((figures_dir /  'train_metrics_bootstrap.png'), Align.C, h=pdf.eph//3.5, w=pdf.epw-20)
    pdf.add_page()

    pdf.image((figures_dir / 'feature_importance.png'), Align.C, w=pdf.epw-20)
    pdf.add_page()

    if problem_type in ['binary', 'multiclass']:
        pdf.image((figures_dir / 'model_evaluation.png'), Align.C, w=pdf.epw-20)
        pdf.image((figures_dir / 'confusion_matrix.png'), Align.C, h=pdf.eph/2, w=pdf.epw-20)
        pdf.add_page()

        pdf.image((figures_dir / 'shap_barplot.png'), Align.C, h=pdf.eph/2, w=pdf.epw-20)
        pdf.image((output_dir /  'figures' / 'shap_heatmap.png'), Align.C, h=pdf.eph/2, w=pdf.epw-20)
    elif problem_type == 'regression':
        pdf.image((figures_dir / 'residual_plot.png'), Align.C, h=pdf.eph/2, w=pdf.epw-20)
        pdf.image((output_dir /  'figures' / 'true_vs_predicted.png'), Align.C, h=pdf.eph/2, w=pdf.epw-20)

    # Save PDF
    pdf.output((output_dir / 'explainer_report.pdf'))