Adjust tutorial dependencies after colab image update (#9)

* 🎨 sort and format inputs

* 🎨 latest colab image has finally pandas 2 (2.0.3)

- restriction on matplotlib version not needed anymore

- 🎨 shorten "principal comentent" to PC

docs/tutorial/log_reg.ipynb

@@ -27,8 +27,7 @@
    "source": [
     "# Setup colab installation\n",
     "# You need to restart the runtime after running this cell\n",
-    "# (due to a pandas 1.5.3 and matplotlib >3.7 incompability - 23-11-07)\n",
-    "%pip install njab heatmapz openpyxl \"matplotlib<3.7\" plotly"
+    "%pip install njab heatmapz openpyxl plotly"
    ]
   },
   {
@@ -47,32 +46,28 @@
     "from pathlib import Path\n",
     "from typing import Optional\n",
     "\n",
-    "from IPython.display import display\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "\n",
     "import plotly.express as px\n",
-    "import matplotlib.pyplot as plt\n",
     "import seaborn\n",
-    "from heatmap import corrplot\n",
-    "import umap\n",
-    "\n",
     "import sklearn\n",
     "import sklearn.impute\n",
-    "from sklearn.metrics import make_scorer, log_loss\n",
     "import statsmodels.api as sm\n",
+    "import umap\n",
+    "from heatmap import corrplot\n",
+    "from IPython.display import display\n",
+    "from sklearn.metrics import log_loss, make_scorer\n",
     "\n",
     "import njab.sklearn\n",
+    "from njab.plotting.metrics import plot_auc, plot_prc\n",
     "from njab.sklearn import StandardScaler\n",
     "from njab.sklearn import pca as njab_pca\n",
-    "from njab.sklearn.scoring import ConfusionMatrix\n",
+    "from njab.sklearn.scoring import (ConfusionMatrix,\n",
+    "                                  get_lr_multiplicative_decomposition,\n",
+    "                                  get_pred, get_score,\n",
+    "                                  get_target_count_per_bin)\n",
     "from njab.sklearn.types import Splits\n",
-    "from njab.plotting.metrics import plot_auc, plot_prc\n",
-    "from njab.sklearn.scoring import (get_score,\n",
-    "                                  get_pred,\n",
-    "                                  get_target_count_per_bin,\n",
-    "                                  get_lr_multiplicative_decomposition)\n",
     "\n",
     "logger = logging.getLogger('njab')\n",
     "logger.setLevel(logging.INFO)\n",
@@ -588,6 +583,7 @@
     "files_out['scatter_first_5PCs.pdf'] = FOLDER / 'scatter_first_5PCs.pdf'\n",
     "\n",
     "fig, axes = plt.subplots(5, 2, figsize=(6, 8), layout='constrained')\n",
+    "PCs.columns = [s.replace(\"principal component\", \"PC\") for s in PCs.columns]\n",
     "PCs = PCs.join(y.astype('category'))\n",
     "up_to = min(PCs.shape[-1], 5)\n",
     "# https://github.com/matplotlib/matplotlib/issues/25538\n",

docs/tutorial/log_reg.py

@@ -25,41 +25,36 @@
 # %% tags=["hide-output"]
 # Setup colab installation
 # You need to restart the runtime after running this cell
-# (due to a pandas 1.5.3 and matplotlib >3.7 incompability - 23-11-07)
-# %pip install njab heatmapz openpyxl "matplotlib<3.7" plotly
+# %pip install njab heatmapz openpyxl plotly
 
 # %% tags=["hide-input"]
 import itertools
 import logging
 from pathlib import Path
 from typing import Optional
 
-from IPython.display import display
-
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-
 import plotly.express as px
-import matplotlib.pyplot as plt
 import seaborn
-from heatmap import corrplot
-import umap
-
 import sklearn
 import sklearn.impute
-from sklearn.metrics import make_scorer, log_loss
 import statsmodels.api as sm
+import umap
+from heatmap import corrplot
+from IPython.display import display
+from sklearn.metrics import log_loss, make_scorer
 
 import njab.sklearn
+from njab.plotting.metrics import plot_auc, plot_prc
 from njab.sklearn import StandardScaler
 from njab.sklearn import pca as njab_pca
-from njab.sklearn.scoring import ConfusionMatrix
+from njab.sklearn.scoring import (ConfusionMatrix,
+                                  get_lr_multiplicative_decomposition,
+                                  get_pred, get_score,
+                                  get_target_count_per_bin)
 from njab.sklearn.types import Splits
-from njab.plotting.metrics import plot_auc, plot_prc
-from njab.sklearn.scoring import (get_score,
-                                  get_pred,
-                                  get_target_count_per_bin,
-                                  get_lr_multiplicative_decomposition)
 
 logger = logging.getLogger('njab')
 logger.setLevel(logging.INFO)
@@ -289,6 +284,7 @@
 files_out['scatter_first_5PCs.pdf'] = FOLDER / 'scatter_first_5PCs.pdf'
 
 fig, axes = plt.subplots(5, 2, figsize=(6, 8), layout='constrained')
+PCs.columns = [s.replace("principal component", "PC") for s in PCs.columns]
 PCs = PCs.join(y.astype('category'))
 up_to = min(PCs.shape[-1], 5)
 # https://github.com/matplotlib/matplotlib/issues/25538