+Stacked bars
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+In [1]:
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+ Copied!
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+ from great_ai import query_ground_truth
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+data = query_ground_truth("train")
+from great_ai import query_ground_truth
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+data = query_ground_truth("train")
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+Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️ +Cannot find credentials files, defaulting to using ParallelTinyDbDriver +The selected tracing database (ParallelTinyDbDriver) is not recommended for production +Cannot find credentials files, defaulting to using LargeFileLocal +GreatAI (v0.1.6): configured ✅ + 🔩 tracing_database: ParallelTinyDbDriver + 🔩 large_file_implementation: LargeFileLocal + 🔩 is_production: False + 🔩 should_log_exception_stack: True + 🔩 prediction_cache_size: 512 + 🔩 dashboard_table_size: 50 +You still need to check whether you follow all best practices before trusting your deployment. +> Find out more at https://se-ml.github.io/practices ++
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+In [9]:
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+ Copied!
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+ from collections import Counter
+import matplotlib.pyplot as plt
+from great_ai.utilities import unique
+import numpy as np
+import matplotlib.ticker as mtick
+
+
+first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0)
+second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1)
+third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2)
+
+domains = sorted(unique(domain for d in data for domain in d.feedback))
+
+first = [first[d] / len(data) for d in domains]
+second = [second[d] / len(data) for d in domains]
+third = [third[d] / len(data) for d in domains]
+
+# Configure matplotlib to have nice, high-resolution charts
+%matplotlib inline
+plt.rcParams["figure.facecolor"] = "white"
+plt.rcParams["font.size"] = 20
+plt.rcParams["figure.figsize"] = (14, 10)
+
+fig, ax = plt.subplots()
+
+plt.xticks(rotation=90)
+
+ax.bar(domains, first, label="Primary domain")
+ax.bar(domains, second, label="Secondary domain", bottom=first)
+ax.bar(domains, third, label="Tertiary domain", bottom=np.add(first, second))
+ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))
+ax.legend()
+ax.set_ylabel("Ratio of all publications")
+fig.tight_layout()
+fig.savefig("ss-distribution.png", dpi=500)
+None
+from collections import Counter
+import matplotlib.pyplot as plt
+from great_ai.utilities import unique
+import numpy as np
+import matplotlib.ticker as mtick
+
+
+first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0)
+second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1)
+third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2)
+
+domains = sorted(unique(domain for d in data for domain in d.feedback))
+
+first = [first[d] / len(data) for d in domains]
+second = [second[d] / len(data) for d in domains]
+third = [third[d] / len(data) for d in domains]
+
+# Configure matplotlib to have nice, high-resolution charts
+%matplotlib inline
+plt.rcParams["figure.facecolor"] = "white"
+plt.rcParams["font.size"] = 20
+plt.rcParams["figure.figsize"] = (14, 10)
+
+fig, ax = plt.subplots()
+
+plt.xticks(rotation=90)
+
+ax.bar(domains, first, label="Primary domain")
+ax.bar(domains, second, label="Secondary domain", bottom=first)
+ax.bar(domains, third, label="Tertiary domain", bottom=np.add(first, second))
+ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))
+ax.legend()
+ax.set_ylabel("Ratio of all publications")
+fig.tight_layout()
+fig.savefig("ss-distribution.png", dpi=500)
+None
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+ Last update:
+ July 16, 2022
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+ 
@@ -2197,481 +2197,25 @@ results.sort_values("rank_test_score")