Merge pull request #116 from Nixtla/fix/deprecate-mycolorpy

[FIX] Deprecate mycolorpy dependency
Nixtla · Nov 30, 2022 · 339131a · 339131a
2 parents f4f7111 + 173bd50
commit 339131a
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Showing 4 changed files with 17 additions and 16 deletions.
diff --git a/environment.yml b/environment.yml
@@ -7,8 +7,7 @@ dependencies:
   - numba
   - pandas
   - scikit-learn
+  - quadprog
   - pip
   - pip:
     - nbdev
-    - mycolorpy
-    - quadprog
diff --git a/hierarchicalforecast/utils.py b/hierarchicalforecast/utils.py
@@ -12,8 +12,6 @@
 import numpy as np
 import pandas as pd
 from sklearn.preprocessing import OneHotEncoder
-# from matplotlib import rcParams
-from mycolorpy import colorlist as mcp
 
 plt.rcParams['font.family'] = 'serif'
 
@@ -124,7 +122,8 @@ def plot_series(self,
         df_plot = Y_df.loc[series].set_index('ds')
         cols = models if models is not None else df_plot.columns
         cols_wo_levels = [col for col in cols if ('lo' not in col and 'hi' not in col)]
-        cmap = mcp.gen_color('tab10', 10)[:len(cols_wo_levels)]
+        cmap = plt.cm.get_cmap("tab10", 10)
+        cmap = [cmap(i) for i in range(10)][:len(cols_wo_levels)]
         cmap_dict = dict(zip(cols_wo_levels, cmap))
         df_plot[cols_wo_levels].plot(ax=ax, linewidth=2, color=cmap)
         if level is not None:
@@ -142,7 +141,7 @@ def plot_series(self,
                         df_plot.index, 
                         df_plot[f'{col}-lo-{lv}'], 
                         df_plot[f'{col}-hi-{lv}'],
-                        alpha=-lv/50 + 2,
+                        alpha=-lv/100 + 1,
                         color=cmap_dict[col],
                         label=f'{col}_level_{lv}'
                     )
@@ -177,7 +176,8 @@ def plot_hierarchically_linked_series(self,
         fig, axs = plt.subplots(len(linked_series), 1, figsize=(20, 2 * len(linked_series)))
         cols = models if models is not None else Y_df.drop(['ds'], axis=1)
         cols_wo_levels = [col for col in cols if ('lo' not in col and 'hi' not in col)]
-        cmap = mcp.gen_color('tab10', 10)[:len(cols_wo_levels)]
+        cmap = plt.cm.get_cmap("tab10", 10)
+        cmap = [cmap(i) for i in range(10)][:len(cols_wo_levels)]
         cmap_dict = dict(zip(cols_wo_levels, cmap))
         for idx, series in enumerate(linked_series):
             df_plot = Y_df.loc[[series]].set_index('ds')
@@ -197,7 +197,7 @@ def plot_hierarchically_linked_series(self,
                             df_plot.index, 
                             df_plot[f'{col}-lo-{lv}'], 
                             df_plot[f'{col}-hi-{lv}'],
-                            alpha=-lv/50 + 2,
+                            alpha=-lv/100 + 1,
                             color=cmap_dict[col],
                             label=f'{col}_level_{lv}'
                         )

diff --git a/nbs/utils.ipynb b/nbs/utils.ipynb
@@ -39,8 +39,6 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "from sklearn.preprocessing import OneHotEncoder\n",
-    "# from matplotlib import rcParams\n",
-    "from mycolorpy import colorlist as mcp\n",
     "\n",
     "plt.rcParams['font.family'] = 'serif'"
    ]
@@ -239,7 +237,8 @@
     "        df_plot = Y_df.loc[series].set_index('ds')\n",
     "        cols = models if models is not None else df_plot.columns\n",
     "        cols_wo_levels = [col for col in cols if ('lo' not in col and 'hi' not in col)]\n",
-    "        cmap = mcp.gen_color('tab10', 10)[:len(cols_wo_levels)]\n",
+    "        cmap = plt.cm.get_cmap(\"tab10\", 10)\n",
+    "        cmap = [cmap(i) for i in range(10)][:len(cols_wo_levels)]\n",
     "        cmap_dict = dict(zip(cols_wo_levels, cmap))\n",
     "        df_plot[cols_wo_levels].plot(ax=ax, linewidth=2, color=cmap)\n",
     "        if level is not None:\n",
@@ -257,7 +256,7 @@
     "                        df_plot.index, \n",
     "                        df_plot[f'{col}-lo-{lv}'], \n",
     "                        df_plot[f'{col}-hi-{lv}'],\n",
-    "                        alpha=-lv/50 + 2,\n",
+    "                        alpha=-lv/100 + 1,\n",
     "                        color=cmap_dict[col],\n",
     "                        label=f'{col}_level_{lv}'\n",
     "                    )\n",
@@ -292,7 +291,8 @@
     "        fig, axs = plt.subplots(len(linked_series), 1, figsize=(20, 2 * len(linked_series)))\n",
     "        cols = models if models is not None else Y_df.drop(['ds'], axis=1)\n",
     "        cols_wo_levels = [col for col in cols if ('lo' not in col and 'hi' not in col)]\n",
-    "        cmap = mcp.gen_color('tab10', 10)[:len(cols_wo_levels)]\n",
+    "        cmap = plt.cm.get_cmap(\"tab10\", 10)\n",
+    "        cmap = [cmap(i) for i in range(10)][:len(cols_wo_levels)]\n",
     "        cmap_dict = dict(zip(cols_wo_levels, cmap))\n",
     "        for idx, series in enumerate(linked_series):\n",
     "            df_plot = Y_df.loc[[series]].set_index('ds')\n",
@@ -312,7 +312,7 @@
     "                            df_plot.index, \n",
     "                            df_plot[f'{col}-lo-{lv}'], \n",
     "                            df_plot[f'{col}-hi-{lv}'],\n",
-    "                            alpha=-lv/50 + 2,\n",
+    "                            alpha=-lv/100 + 1,\n",
     "                            color=cmap_dict[col],\n",
     "                            label=f'{col}_level_{lv}'\n",
     "                        )\n",
@@ -459,6 +459,8 @@
     "hier_df['Model'] = hier_df['y'] * 1.1\n",
     "hier_df['Model-lo-80'] = hier_df['Model'] - 0.1 * hier_df['Model']\n",
     "hier_df['Model-hi-80'] = hier_df['Model'] + 0.1 * hier_df['Model']\n",
+    "hier_df['Model-lo-90'] = hier_df['Model'] - 0.2 * hier_df['Model']\n",
+    "hier_df['Model-hi-90'] = hier_df['Model'] + 0.2 * hier_df['Model']\n",
     "hplots.plot_series(\n",
     "    series='Australia', \n",
     "    Y_df=hier_df,\n",

diff --git a/settings.ini b/settings.ini
@@ -15,8 +15,8 @@ language = English
 custom_sidebar = True
 license = apache2
 status = 2
-requirements = numpy numba pandas scikit-learn mycolorpy quadprog
-dev_requirements = datasetsforecast statsforecast>=1.0.0 requests matplotlib
+requirements = numpy numba pandas scikit-learn quadprog matplotlib
+dev_requirements = datasetsforecast statsforecast>=1.0.0 requests
 nbs_path = nbs
 doc_path = _docs
 recursive = True