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Fixed the plot.ipynb so that best prediction file per group could be … #13

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Fixed the plot.ipynb so that best prediction file per group could be … #13

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Fixed the plot.ipynb so that best prediction file per group could be …
Mar 22, 2024
0211c7e
Merge branch 'main' of github.com:RashikaRamola/CAFA-evaluator into main
May 18, 2024
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56 changes: 24 additions & 32 deletions src/plot.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -44,18 +44,16 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Map column names to full names (for axis labels)\n",
"axis_title_dict = {'pr': 'Precision', 'rc': 'Recall', 'f': 'F-score', 'pr_w': 'Weighted Precision', 'rc_w': 'Weighted Recall', 'f_w': 'Weighted F-score', 'mi': 'Misinformation (Unweighted)', 'ru': 'Remaining Uncertainty (Unweighted)', 'mi_w': 'Misinformation', 'ru_w': 'Remaining Uncertainty', 's': 'S-score', 'pr_micro': 'Precision (Micro)', 'rc_micro': 'Recall (Micro)', 'f_micro': 'F-score (Micro)', 'pr_micro_w': 'Weighted Precision (Micro)', 'rc_micro_w': 'Weighted Recall (Micro)', 'f_micro_w': 'Weighted F-score (Micro)'}\n",
"\n",
"# Map ontology namespaces to full names (for plot titles)\n",
"ontology_dict = {'biological_process': 'BPO', 'molecular_function': 'MFO', 'cellular_component': 'CCO'}"
],
"metadata": {
"collapsed": false
},
"execution_count": null
]
},
{
"cell_type": "code",
Expand Down Expand Up @@ -88,7 +86,7 @@
" else:\n",
" df['is_baseline'].fillna(False, inplace=True)\n",
" # print(methods)\n",
"df = df.drop(columns='filename').set_index(['group', 'label', 'ns', 'tau'])\n",
"df = df.set_index(['group', 'label', 'ns', 'filename','tau'])\n",
"df"
]
},
Expand All @@ -105,6 +103,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Assign colors based on group\n",
Expand All @@ -113,9 +113,7 @@
"df['colors'] = pd.factorize(df['colors'])[0]\n",
"df['colors'] = df['colors'].apply(lambda x: cmap.colors[x % len(cmap.colors)])\n",
"df"
],
"metadata": {},
"execution_count": null
]
},
{
"cell_type": "code",
Expand Down Expand Up @@ -152,6 +150,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Add first last points to precision and recall curves to improve APS calculation\n",
Expand All @@ -165,47 +165,41 @@
"if metric.startswith('f') and add_extreme_points:\n",
" df_methods = df_methods.reset_index().groupby(['group', 'label', 'ns'], as_index=False).apply(add_points).set_index(['group', 'label', 'ns'])\n",
"df_methods"
],
"metadata": {
"collapsed": false
},
"execution_count": null
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Filter the dataframe for the best method and threshold\n",
"df_best = df.loc[index_best, ['cov', 'colors'] + cols + [metric]]\n",
"df_best"
],
"metadata": {},
"execution_count": null
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculate average precision score \n",
"if metric.startswith('f'):\n",
" df_best['aps'] = df_methods.groupby(level=['group', 'label', 'ns'])[[cols[0], cols[1]]].apply(lambda x: (x[cols[0]].diff(-1).shift(1) * x[cols[1]]).sum())\n",
"df_best"
],
"metadata": {
"collapsed": false
},
"execution_count": null
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculate the max coverage across all thresholds\n",
"df_best['max_cov'] = df_methods.groupby(level=['group', 'label', 'ns'])['cov'].max()\n",
"df_best"
],
"metadata": {},
"execution_count": null
]
},
{
"cell_type": "code",
Expand Down Expand Up @@ -247,7 +241,7 @@
"\n",
" # Iterate methods\n",
" for i, (index, row) in enumerate(df_g.sort_values(by=[metric, 'max_cov'], ascending=[False if metric.startswith('f') else True, False]).iterrows()):\n",
" data = df_methods.loc[index[:-1]]\n",
" data = df_methods.loc[index[:-2]]\n",
" \n",
" # Precision-recall or mi-ru curves\n",
" ax.plot(data[cols[0]], data[cols[1]], color=row['colors'], label=row['label'], lw=2, zorder=500-i)\n",
Expand Down Expand Up @@ -282,12 +276,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
},
"execution_count": null
"source": []
}
],
"metadata": {
Expand All @@ -306,7 +298,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
"version": "3.10.9"
}
},
"nbformat": 4,
Expand Down