Stacked bars

In [1]:

from great_ai import query_ground_truth

data = query_ground_truth("train")

from great_ai import query_ground_truth data = query_ground_truth("train")

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

In [9]:

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