update poetry; rerun all notebooks (#762)

Yorko · Jun 27, 2024 · a1638e7 · a1638e7
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diff --git a/mlcourse_ai_jupyter_book/CNAME b/mlcourse_ai_jupyter_book/CNAME
diff --git a/mlcourse_ai_jupyter_book/_config.yml b/mlcourse_ai_jupyter_book/_config.yml
diff --git a/mlcourse_ai_jupyter_book/_toc.yml b/mlcourse_ai_jupyter_book/_toc.yml
diff --git a/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree1.png b/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree1.png
diff --git a/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree2.png b/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree2.png
diff --git a/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree3.png b/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree3.png
diff --git a/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree4.png b/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree4.png
diff --git a/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree5.png b/mlcourse_ai_jupyter_book/book/topic03/topic3_decision_tree5.png
diff --git a/mlcourse_ai_jupyter_book/book/topic07/topic7_pca_clustering.md b/mlcourse_ai_jupyter_book/book/topic07/topic7_pca_clustering.md
@@ -227,7 +227,7 @@ plt.scatter(
     edgecolor="none",
     alpha=0.7,
     s=40,
-    cmap=plt.cm.get_cmap("nipy_spectral", 10),
+    cmap=plt.get_cmap("nipy_spectral", 10),
 )
 plt.colorbar()
 plt.title("MNIST. PCA projection");
@@ -253,7 +253,7 @@ plt.scatter(
     edgecolor="none",
     alpha=0.7,
     s=40,
-    cmap=plt.cm.get_cmap("nipy_spectral", 10),
+    cmap=plt.get_cmap("nipy_spectral", 10),
 )
 plt.colorbar()
 plt.title("MNIST. t-SNE projection");

diff --git a/mlcourse_ai_jupyter_book/book/topic08/topic08_sgd_hashing_vowpal_wabbit.md b/mlcourse_ai_jupyter_book/book/topic08/topic08_sgd_hashing_vowpal_wabbit.md
@@ -350,7 +350,7 @@ Shell is the main interface for VW.
 
 
 ```{code-cell} ipython3
-!vw --help | head
+#!vw --help | head
 ```
 
 Vowpal Wabbit reads data from files or from standard input stream (stdin) with the following format:
@@ -720,8 +720,8 @@ with open(os.path.join(PATH_TO_WRITE_DATA, "movie_reviews_test.vw"), "w") as vw_
 
 
 ```{code-cell} ipython3
-!vw -d $PATH_TO_WRITE_DATA/movie_reviews_train.vw --loss_function hinge \
--f $PATH_TO_WRITE_DATA/movie_reviews_model.vw --quiet
+#!vw -d $PATH_TO_WRITE_DATA/movie_reviews_train.vw --loss_function hinge \
+#-f $PATH_TO_WRITE_DATA/movie_reviews_model.vw --quiet
 ```
 
 Next, make the hold-out prediction with the following VW arguments:
@@ -732,8 +732,8 @@ Next, make the hold-out prediction with the following VW arguments:
 
 
 ```{code-cell} ipython3
-!vw -i $PATH_TO_WRITE_DATA/movie_reviews_model.vw -t \
--d $PATH_TO_WRITE_DATA/movie_reviews_valid.vw -p $PATH_TO_WRITE_DATA/movie_valid_pred.txt --quiet
+#!vw -i $PATH_TO_WRITE_DATA/movie_reviews_model.vw -t \
+#-d $PATH_TO_WRITE_DATA/movie_reviews_valid.vw -p $PATH_TO_WRITE_DATA/movie_valid_pred.txt --quiet
 ```
 
 Read the predictions from the text file and estimate the accuracy and ROC AUC. Note that VW prints probability estimates of the +1 class. These estimates are distributed from  -1 to 1, so we can convert these into binary answers, assuming that positive values belong to class 1.
@@ -759,9 +759,9 @@ Again, do the same for the test set.
 
 
 ```{code-cell} ipython3
-!vw -i $PATH_TO_WRITE_DATA/movie_reviews_model.vw -t \
--d $PATH_TO_WRITE_DATA/movie_reviews_test.vw \
--p $PATH_TO_WRITE_DATA/movie_test_pred.txt --quiet
+#!vw -i $PATH_TO_WRITE_DATA/movie_reviews_model.vw -t \
+#-d $PATH_TO_WRITE_DATA/movie_reviews_test.vw \
+#-p $PATH_TO_WRITE_DATA/movie_test_pred.txt --quiet
 ```
 
 
@@ -787,14 +787,14 @@ Let's try to achieve a higher accuracy by incorporating bigrams.
 
 
 ```{code-cell} ipython3
-!vw -d $PATH_TO_WRITE_DATA/movie_reviews_train.vw \
---loss_function hinge --ngram 2 -f $PATH_TO_WRITE_DATA/movie_reviews_model2.vw --quiet
+#!vw -d $PATH_TO_WRITE_DATA/movie_reviews_train.vw \
+# --loss_function hinge --ngram 2 -f $PATH_TO_WRITE_DATA/movie_reviews_model2.vw --quiet
 ```
 
 
 ```{code-cell} ipython3
-!vw -i$PATH_TO_WRITE_DATA/movie_reviews_model2.vw -t -d $PATH_TO_WRITE_DATA/movie_reviews_valid.vw \
--p $PATH_TO_WRITE_DATA/movie_valid_pred2.txt --quiet
+#!vw -i$PATH_TO_WRITE_DATA/movie_reviews_model2.vw -t -d $PATH_TO_WRITE_DATA/movie_reviews_valid.vw \
+#-p $PATH_TO_WRITE_DATA/movie_valid_pred2.txt --quiet
 ```
 
 
@@ -817,8 +817,8 @@ print("AUC: {}".format(round(roc_auc_score(valid_labels, valid_prediction), 3)))
 
 
 ```{code-cell} ipython3
-!vw -i $PATH_TO_WRITE_DATA/movie_reviews_model2.vw -t -d $PATH_TO_WRITE_DATA/movie_reviews_test.vw \
--p $PATH_TO_WRITE_DATA/movie_test_pred2.txt --quiet
+#!vw -i $PATH_TO_WRITE_DATA/movie_reviews_model2.vw -t -d $PATH_TO_WRITE_DATA/movie_reviews_test.vw \
+#-p $PATH_TO_WRITE_DATA/movie_test_pred2.txt --quiet
 ```
 
 
@@ -857,5 +857,4 @@ This section has been moved to Kaggle, please explore [this Notebook](https://ww
 - ["Convex Optimization" by Stephen Boyd](https://www.amazon.com/Convex-Optimization-Stephen-Boyd/dp/0521833787)
 - "Command-line Tools can be 235x Faster than your Hadoop Cluster" [post](https://adamdrake.com/command-line-tools-can-be-235x-faster-than-your-hadoop-cluster.html)
 - Benchmarking various ML algorithms on Criteo 1TB dataset on [GitHub](https://github.com/rambler-digital-solutions/criteo-1tb-benchmark)
-- [VW on FastML.com](http://fastml.com/blog/categories/vw/)
-q
+- [VW on FastML.com](http://fastml.com/blog/categories/vw/)
diff --git a/mlcourse_ai_jupyter_book/book/topic09/assignment09_time_series.md b/mlcourse_ai_jupyter_book/book/topic09/assignment09_time_series.md
@@ -94,7 +94,7 @@ df.date = pd.to_datetime(df.date)
 
 
 ```{code-cell} ipython3
-plotly_df(df.set_index("date")[["count"]])
+plotly_df(df=df.set_index("date")[["count"]], title="assign9_plot")
 ```
 
 

diff --git a/mlcourse_ai_jupyter_book/book/topic09/assignment09_time_series_solution.md b/mlcourse_ai_jupyter_book/book/topic09/assignment09_time_series_solution.md
@@ -94,7 +94,7 @@ df.date = pd.to_datetime(df.date)
 
 
 ```{code-cell} ipython3
-plotly_df(df.set_index("date")[["count"]])
+plotly_df(df=df.set_index("date")[["count"]], title="assign9_plot")
 ```
 
 

diff --git a/mlcourse_ai_jupyter_book/book/topic09/topic9_part1_time_series_python.md b/mlcourse_ai_jupyter_book/book/topic09/topic9_part1_time_series_python.md
@@ -368,7 +368,7 @@ def plotExponentialSmoothing(series, alphas):
         alphas - list of floats, smoothing parameters
 
     """
-    with plt.style.context("seaborn-white"):
+    with plt.style.context("bmh"):
         plt.figure(figsize=(15, 7))
         for alpha in alphas:
             plt.plot(
@@ -437,7 +437,7 @@ def plotDoubleExponentialSmoothing(series, alphas, betas):
         betas - list of floats, smoothing parameters for trend
     """
 
-    with plt.style.context("seaborn-white"):
+    with plt.style.context("bmh"):
         plt.figure(figsize=(20, 8))
         for alpha in alphas:
             for beta in betas:

diff --git a/mlcourse_ai_jupyter_book/prerequisites.html b/mlcourse_ai_jupyter_book/prerequisites.html
diff --git a/mlcourse_ai_jupyter_book/rating.html b/mlcourse_ai_jupyter_book/rating.html
diff --git a/mlcourse_ai_jupyter_book/roadmap.html b/mlcourse_ai_jupyter_book/roadmap.html
diff --git a/mlcourse_ai_jupyter_book/tutorials.html b/mlcourse_ai_jupyter_book/tutorials.html