🎨 allow custom display name of feat

RasmussenLab · Nov 28, 2023 · e49c1eb · e49c1eb
1 parent 49ee8e5
commit e49c1eb
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Showing 3 changed files with 55 additions and 41 deletions.
diff --git a/project/01_2_performance_plots.ipynb b/project/01_2_performance_plots.ipynb
@@ -36,7 +36,6 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import seaborn as sns\n",
     "\n",
     "import vaep\n",
     "import vaep.imputation\n",
@@ -105,7 +104,6 @@
    "execution_count": null,
    "id": "e6e91c6b-20d6-402c-9577-a2bfd8ba592e",
    "metadata": {
-    "lines_to_next_cell": 2,
     "tags": [
      "parameters"
     ]
@@ -122,7 +120,8 @@
     "models: str = 'Median,CF,DAE,VAE'  # picked models to compare (comma separated)\n",
     "sel_models: str = ''  # user defined comparison (comma separated)\n",
     "# Restrict plotting to top N methods for imputation based on error of validation data, maximum 10\n",
-    "plot_to_n: int = 5"
+    "plot_to_n: int = 5\n",
+    "feat_name_display: str = None  # display name for feature name (e.g. 'protein group')"
    ]
   },
   {
@@ -187,6 +186,7 @@
     "MIN_FREQ = None\n",
     "MODELS_PASSED = args.models.split(',')\n",
     "MODELS = MODELS_PASSED.copy()\n",
+    "FEAT_NAME_DISPLAY = args.feat_name_display\n",
     "SEL_MODELS = None\n",
     "if args.sel_models:\n",
     "    SEL_MODELS = args.sel_models.split(',')"
@@ -430,6 +430,9 @@
     "    split='val',\n",
     "    model_keys=MODELS_PASSED,\n",
     "    shared_columns=[TARGET_COL])\n",
+    "SAMPLE_ID, FEAT_NAME = pred_val.index.names\n",
+    "if not FEAT_NAME_DISPLAY:\n",
+    "    FEAT_NAME_DISPLAY = FEAT_NAME\n",
     "pred_val[MODELS]"
    ]
   },
@@ -738,6 +741,7 @@
     "    ],\n",
     "    feat_medians=data.train_X.median(),\n",
     "    ax=ax,\n",
+    "    feat_name=FEAT_NAME_DISPLAY,\n",
     "    palette=TOP_N_COLOR_PALETTE,\n",
     "    metric_name=METRIC,)\n",
     "ax.set_ylabel(f\"Average error ({METRIC})\")\n",
@@ -784,7 +788,7 @@
     "    model_keys=MODELS_PASSED,\n",
     "    shared_columns=[TARGET_COL])\n",
     "pred_test = pred_test.join(freq_feat, on=freq_feat.index.name)\n",
-    "SAMPLE_ID, FEAT_NAME = pred_test.index.names\n",
+    "\n",
     "pred_test"
    ]
   },
@@ -1103,7 +1107,7 @@
    "source": [
     "kwargs = dict(rot=90,\n",
     "              flierprops=dict(markersize=1),\n",
-    "              ylabel=f'correlation per {FEAT_NAME}')\n",
+    "              ylabel=f'correlation per {FEAT_NAME_DISPLAY}')\n",
     "ax = (corr_per_feat_test\n",
     "      .loc[~too_few_obs, TOP_N_ORDER]\n",
     "      .plot\n",
@@ -1255,7 +1259,7 @@
     "fig, ax = plt.subplots(figsize=(4, 2))\n",
     "ax = _to_plot.loc[[feature_names.name]].plot.bar(\n",
     "    rot=0,\n",
-    "    ylabel=f\"{METRIC} for {feature_names.name} ({n_in_comparison:,} intensities)\",\n",
+    "    ylabel=f\"{METRIC} for {FEAT_NAME_DISPLAY} ({n_in_comparison:,} intensities)\",\n",
     "    # title=f'performance on test data (based on {n_in_comparison:,} measurements)',\n",
     "    color=COLORS_TO_USE,\n",
     "    ax=ax,\n",
@@ -1313,6 +1317,7 @@
     "    ],\n",
     "    feat_medians=data.train_X.median(),\n",
     "    ax=ax,\n",
+    "    feat_name=FEAT_NAME_DISPLAY,\n",
     "    metric_name=METRIC,\n",
     "    palette=COLORS_TO_USE\n",
     ")\n",
@@ -1326,6 +1331,23 @@
     "errors_binned"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b13ecd37",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "(errors_binned\n",
+    " .set_index(\n",
+    "     ['model', errors_binned.columns[-1]]\n",
+    " )\n",
+    " .loc[ORDER_MODELS[0]]\n",
+    " .sort_values(by=METRIC))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "26370a1a",
@@ -1372,7 +1394,7 @@
     "    fig, ax = plt.subplots(figsize=(4, 2))\n",
     "    ax = _to_plot.loc[[feature_names.name]].plot.bar(\n",
     "        rot=0,\n",
-    "        ylabel=f\"{METRIC} for {feature_names.name} ({n_in_comparison:,} intensities)\",\n",
+    "        ylabel=f\"{METRIC} for {FEAT_NAME_DISPLAY} ({n_in_comparison:,} intensities)\",\n",
     "        # title=f'performance on test data (based on {n_in_comparison:,} measurements)',\n",
     "        color=vaep.plotting.defaults.assign_colors(\n",
     "            list(k.upper() for k in SEL_MODELS)),\n",
@@ -1413,6 +1435,7 @@
     "        feat_medians=data.train_X.median(),\n",
     "        ax=ax,\n",
     "        metric_name=METRIC,\n",
+    "        feat_name=FEAT_NAME_DISPLAY,\n",
     "        palette=vaep.plotting.defaults.assign_colors(\n",
     "            list(k.upper() for k in SEL_MODELS))\n",
     "    )\n",
@@ -1429,23 +1452,6 @@
     "    display(errors_binned)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b13ecd37",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "(errors_binned\n",
-    " .set_index(\n",
-    "     ['model', errors_binned.columns[-1]]\n",
-    " )\n",
-    " .loc[ORDER_MODELS[0]]\n",
-    " .sort_values(by=METRIC))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "549236ca-9e89-47aa-905c-c97a45d4dc2b",

diff --git a/project/01_2_performance_plots.py b/project/01_2_performance_plots.py
@@ -36,7 +36,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import seaborn as sns
 
 import vaep
 import vaep.imputation
@@ -98,7 +97,7 @@ def build_text(s):
 sel_models: str = ''  # user defined comparison (comma separated)
 # Restrict plotting to top N methods for imputation based on error of validation data, maximum 10
 plot_to_n: int = 5
-
+feat_name_display: str = None  # display name for feature name (e.g. 'protein group')
 
 # %% [markdown]
 # Some argument transformations
@@ -121,6 +120,7 @@ def build_text(s):
 MIN_FREQ = None
 MODELS_PASSED = args.models.split(',')
 MODELS = MODELS_PASSED.copy()
+FEAT_NAME_DISPLAY = args.feat_name_display
 SEL_MODELS = None
 if args.sel_models:
     SEL_MODELS = args.sel_models.split(',')
@@ -227,6 +227,9 @@ def build_text(s):
     split='val',
     model_keys=MODELS_PASSED,
     shared_columns=[TARGET_COL])
+SAMPLE_ID, FEAT_NAME = pred_val.index.names
+if not FEAT_NAME_DISPLAY:
+    FEAT_NAME_DISPLAY = FEAT_NAME
 pred_val[MODELS]
 
 # %% [markdown]
@@ -370,6 +373,7 @@ def build_text(s):
     ],
     feat_medians=data.train_X.median(),
     ax=ax,
+    feat_name=FEAT_NAME_DISPLAY,
     palette=TOP_N_COLOR_PALETTE,
     metric_name=METRIC,)
 ax.set_ylabel(f"Average error ({METRIC})")
@@ -393,7 +397,7 @@ def build_text(s):
     model_keys=MODELS_PASSED,
     shared_columns=[TARGET_COL])
 pred_test = pred_test.join(freq_feat, on=freq_feat.index.name)
-SAMPLE_ID, FEAT_NAME = pred_test.index.names
+
 pred_test
 
 # %% [markdown]
@@ -553,7 +557,7 @@ def build_text(s):
 # %%
 kwargs = dict(rot=90,
               flierprops=dict(markersize=1),
-              ylabel=f'correlation per {FEAT_NAME}')
+              ylabel=f'correlation per {FEAT_NAME_DISPLAY}')
 ax = (corr_per_feat_test
       .loc[~too_few_obs, TOP_N_ORDER]
       .plot
@@ -635,7 +639,7 @@ def highlight_min(s, color, tolerence=0.00001):
 fig, ax = plt.subplots(figsize=(4, 2))
 ax = _to_plot.loc[[feature_names.name]].plot.bar(
     rot=0,
-    ylabel=f"{METRIC} for {feature_names.name} ({n_in_comparison:,} intensities)",
+    ylabel=f"{METRIC} for {FEAT_NAME_DISPLAY} ({n_in_comparison:,} intensities)",
     # title=f'performance on test data (based on {n_in_comparison:,} measurements)',
     color=COLORS_TO_USE,
     ax=ax,
@@ -670,6 +674,7 @@ def highlight_min(s, color, tolerence=0.00001):
     ],
     feat_medians=data.train_X.median(),
     ax=ax,
+    feat_name=FEAT_NAME_DISPLAY,
     metric_name=METRIC,
     palette=COLORS_TO_USE
 )
@@ -682,6 +687,14 @@ def highlight_min(s, color, tolerence=0.00001):
 errors_binned.to_csv(fname.with_suffix('.csv'))
 errors_binned
 
+# %%
+(errors_binned
+ .set_index(
+     ['model', errors_binned.columns[-1]]
+ )
+ .loc[ORDER_MODELS[0]]
+ .sort_values(by=METRIC))
+
 # %% [markdown]
 # ### Custom model selection
 
@@ -715,7 +728,7 @@ def highlight_min(s, color, tolerence=0.00001):
     fig, ax = plt.subplots(figsize=(4, 2))
     ax = _to_plot.loc[[feature_names.name]].plot.bar(
         rot=0,
-        ylabel=f"{METRIC} for {feature_names.name} ({n_in_comparison:,} intensities)",
+        ylabel=f"{METRIC} for {FEAT_NAME_DISPLAY} ({n_in_comparison:,} intensities)",
         # title=f'performance on test data (based on {n_in_comparison:,} measurements)',
         color=vaep.plotting.defaults.assign_colors(
             list(k.upper() for k in SEL_MODELS)),
@@ -750,6 +763,7 @@ def highlight_min(s, color, tolerence=0.00001):
         feat_medians=data.train_X.median(),
         ax=ax,
         metric_name=METRIC,
+        feat_name=FEAT_NAME_DISPLAY,
         palette=vaep.plotting.defaults.assign_colors(
             list(k.upper() for k in SEL_MODELS))
     )
@@ -765,14 +779,6 @@ def highlight_min(s, color, tolerence=0.00001):
     # ax.xaxis.set_tick_params(rotation=0) # horizontal
     display(errors_binned)
 
-# %%
-(errors_binned
- .set_index(
-     ['model', errors_binned.columns[-1]]
- )
- .loc[ORDER_MODELS[0]]
- .sort_values(by=METRIC))
-
 # %% [markdown]
 # ### Error by non-decimal number of intensity
 #

diff --git a/vaep/plotting/errors.py b/vaep/plotting/errors.py
@@ -52,6 +52,7 @@ def plot_errors_by_median(pred: pd.DataFrame,
                           target_col='observed',
                           ax: Axes = None,
                           palette: dict = None,
+                          feat_name: str = None,
                           metric_name: Optional[str] = None,
                           errwidth: float = 1.2) -> tuple[Axes, pd.DataFrame]:
     # calculate absolute errors
@@ -74,9 +75,10 @@ def plot_errors_by_median(pred: pd.DataFrame,
 
     errors = errors.join(n_obs, on="bin")
 
-    feat_name = feat_medians.index.name
-    if not feat_name:
-        feat_name = 'feature'
+    if feat_name is None:
+        feat_name = feat_medians.index.name
+        if not feat_name:
+            feat_name = 'feature'
 
     x_axis_name = f'intensity binned by median of {feat_name}'
     len_max_bin = len(str(int(errors['bin'].max())))