<jupyter_start><jupyter_text>scikit tree sizes per config per dataset<jupyter_code>max_nodes = dfs.loc[dfs.config == 'none'].groupby(["dataset"])["n_nodes"].mean()

data = dfs.groupby(["config", "dataset"])["n_nodes"].mean().unstack() / max_nodes
#     print(dfs.groupby(["config", "dataset"])[param].mean().unstack())
f, ax = plt.subplots(figsize=(12, 6))
sns.heatmap(data, annot=True, fmt=".0%", vmin=0, vmax=1, ax=ax, cmap='Greys', cbar=False)
plt.savefig("../pruneabletree/tests/results/pruning/heatmap_n_nodes.pdf", bbox_inches="tight")<jupyter_output><empty_output><jupyter_text>scikit vs weka tree size<jupyter_code>nodes_s = dfs.loc[dfs.config != "early_stop"].groupby(["config", "dataset"]).n_nodes.mean().unstack()
nodes_w = dfw.groupby(["config", "dataset"]).n_nodes.mean().unstack()
data = (nodes_s - nodes_w)
f, ax = plt.subplots(figsize=(12, 6))
sns.heatmap(data, annot=True, fmt=".0f", ax=ax, cmap='RdBu', cbar=False, vmin=-156, vmax=156)
plt.savefig("../pruneabletree/tests/results/pruning/heatmap_n_nodes_diff.pdf", bbox_inches="tight")

# max_nodes_s = dfs.loc[dfs.config == 'none'].groupby(["dataset"])["n_nodes"].mean()
# max_nodes_w = dfw.loc[dfw.config == 'none'].groupby(["dataset"])["n_nodes"].mean()

# nodes_s = dfs.loc[dfs.config != "early_stop"].groupby(["config", "dataset"]).n_nodes.mean().unstack() / max_nodes_s
# nodes_w = dfw.groupby(["config", "dataset"]).n_nodes.mean().unstack() / max_nodes_w
# data = nodes_s - nodes_w
# f, ax = plt.subplots(figsize=(12, 6))
# sns.heatmap(data, annot=True, fmt=".0%", ax=ax, cmap='Greys', cbar=False, vmin=-0.10, vmax=.25)
data = df.loc[df.config == "none"].groupby(["tool", "dataset"]).n_nodes.mean().unstack()
f, ax = plt.subplots(figsize=(12, 2))
sns.heatmap(data, annot=True, fmt=".0f", vmin=0, ax=ax, cmap='Greys', cbar=False)
plt.savefig("../pruneabletree/tests/results/pruning/heatmap_n_nodes_unpruned.pdf", bbox_inches="tight")<jupyter_output><empty_output><jupyter_text>scikit accuracy<jupyter_code>data = dfs.groupby(["config", "dataset"])["accuracy"].mean().unstack()
f, ax = plt.subplots(figsize=(12, 6))
sns.heatmap(data, annot=True, fmt=".0%", vmin=0.5, vmax=1, ax=ax, cmap='gray', cbar=False)
plt.savefig("../pruneabletree/tests/results/pruning/heatmap_accuracy.pdf", bbox_inches="tight")<jupyter_output><empty_output><jupyter_text>scikit vs weka accuracy<jupyter_code>data_s = dfs.loc[dfs.config != "early_stop"].groupby(["config", "dataset"])["accuracy"].mean().unstack()
data_w = dfw.groupby(["config", "dataset"])["accuracy"].mean().unstack()
data = data_s - data_w
f, ax = plt.subplots(figsize=(12, 6))
sns.heatmap(data, annot=True, fmt=".0%", vmin=-0.08, vmax=0.08, ax=ax, cmap='RdBu', cbar=False)
plt.savefig("../pruneabletree/tests/results/pruning/heatmap_accuracy_diff.pdf", bbox_inches="tight")<jupyter_output><empty_output><jupyter_text>scikit timings<jupyter_code>dfs.columns
data = dfs.groupby(["config", "dataset"])["fit_time_ms"].mean().unstack()
f, ax = plt.subplots(figsize=(12, 6))
sns.heatmap(data, annot=True, fmt=".0f", ax=ax, cmap='Greys', cbar=False)
plt.savefig("../pruneabletree/tests/results/pruning/heatmap_fit_time.pdf", bbox_inches="tight")

data_s = dfs.loc[dfs.config != "early_stop"].groupby(["config", "dataset"])["fit_time_ms"].mean().unstack()
data_w = dfw.groupby(["config", "dataset"])["fit_time_ms"].mean().unstack()
data = data_s - data_w
f, ax = plt.subplots(figsize=(12, 6))
sns.heatmap(data, annot=True, fmt=".0f", ax=ax, cmap='RdBu', cbar=False, vmin=-1045, vmax=1045)
plt.savefig("../pruneabletree/tests/results/pruning/heatmap_fit_time_diff.pdf", bbox_inches="tight")

# data = dfs.groupby(["config", "dataset"])["score_time_ms"].mean().unstack()
# f, ax = plt.subplots(figsize=(12, 6))
# sns.heatmap(data, annot=True, fmt=".0f", ax=ax, cmap='Greys', cbar=False)

# data = dfw.groupby(["config", "dataset"])["score_time_ms"].mean().unstack()
# f, ax = plt.subplots(figsize=(12, 6))
# sns.heatmap(data, annot=True, fmt=".0f", ax=ax, cmap='Greys', cbar=False)<jupyter_output><empty_output>
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