feature/fix-style-with-pre-commit-all-files

* Fix style in docs files with pre-commit * Fix style in all python files with pre-commit
PlaytikaOSS · Oct 16, 2023 · 026b4c4 · 026b4c4
1 parent 579355b
commit 026b4c4
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Showing 14 changed files with 1,243 additions and 97 deletions.
diff --git a/docs/src/conf.py b/docs/src/conf.py
@@ -13,17 +13,17 @@
 import os
 import sys
 
-sys.path.insert(0, os.path.abspath('..'))
+sys.path.insert(0, os.path.abspath(".."))
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'PyBandits'
-copyright = 'MIT License'
-author = ''
+project = "PyBandits"
+copyright = "MIT License"
+author = ""
 
 # The full version, including alpha/beta/rc tags
-release = '1.0'
+release = "1.0"
 
 
 # -- General configuration ---------------------------------------------------
@@ -32,15 +32,15 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.todo',
-    'sphinx.ext.mathjax',
-    'nbsphinx',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.todo",
+    "sphinx.ext.mathjax",
+    "nbsphinx",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -58,9 +58,9 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ["_static"]
 
 # A list of ignored prefixes for module index sorting.
-modindex_common_prefix = ['pybandits.']
+modindex_common_prefix = ["pybandits."]
 
 nbsphinx_allow_errors = True
diff --git a/docs/src/tutorials/cmab.ipynb b/docs/src/tutorials/cmab.ipynb
@@ -86,7 +86,7 @@
     "n_samples = 1000\n",
     "n_features = 5\n",
     "X = 2 * np.random.random_sample((n_samples, n_features)) - 1  # random float in the interval (-1, 1)\n",
-    "print('X: context matrix of shape (n_samples, n_features)')\n",
+    "print(\"X: context matrix of shape (n_samples, n_features)\")\n",
     "print(X[:10])"
    ]
   },
@@ -97,7 +97,7 @@
    "outputs": [],
    "source": [
     "# define actions\n",
-    "actions_ids= ['action A', 'action B', 'action C']"
+    "actions_ids = [\"action A\", \"action B\", \"action C\"]"
    ]
   },
   {
@@ -140,8 +140,8 @@
    ],
    "source": [
     "# predict action\n",
-    "pred_actions, _ = cmab.predict(X) \n",
-    "print('Recommended action: {}'.format(pred_actions[:10]))"
+    "pred_actions, _ = cmab.predict(X)\n",
+    "print(\"Recommended action: {}\".format(pred_actions[:10]))"
    ]
   },
   {
@@ -167,7 +167,7 @@
    "source": [
     "# simulate reward from environment\n",
     "simulated_rewards = np.random.randint(2, size=n_samples)\n",
-    "print('Simulated rewards: {}'.format(simulated_rewards[:10]))"
+    "print(\"Simulated rewards: {}\".format(simulated_rewards[:10]))"
    ]
   },
   {

diff --git a/docs/src/tutorials/simulation_cmab.ipynb b/docs/src/tutorials/simulation_cmab.ipynb
@@ -47,7 +47,7 @@
     "n_groups = 3\n",
     "n_updates = 5\n",
     "n_jobs = 1\n",
-    "actions_ids = ['action A', 'action B', 'action C']\n",
+    "actions_ids = [\"action A\", \"action B\", \"action C\"]\n",
     "n_features = 5\n",
     "verbose = True"
    ]
@@ -66,11 +66,9 @@
    "outputs": [],
    "source": [
     "# init context matrix and groups\n",
-    "X, group = make_classification(n_samples=batch_size*n_updates,\n",
-    "                               n_features=n_features, \n",
-    "                               n_informative=n_features,\n",
-    "                               n_redundant=0,\n",
-    "                               n_classes=n_groups)"
+    "X, group = make_classification(\n",
+    "    n_samples=batch_size * n_updates, n_features=n_features, n_informative=n_features, n_redundant=0, n_classes=n_groups\n",
+    ")"
    ]
   },
   {
@@ -155,10 +153,10 @@
    ],
    "source": [
     "# init probability of rewards from the environment\n",
-    "prob_rewards = pd.DataFrame([[0.05, 0.80, 0.05],\n",
-    "                             [0.80, 0.05, 0.05],\n",
-    "                             [0.80, 0.05, 0.80]], columns=actions_ids, index=range(n_groups))\n",
-    "print('Probability of positive reward for each group/action:') \n",
+    "prob_rewards = pd.DataFrame(\n",
+    "    [[0.05, 0.80, 0.05], [0.80, 0.05, 0.05], [0.80, 0.05, 0.80]], columns=actions_ids, index=range(n_groups)\n",
+    ")\n",
+    "print(\"Probability of positive reward for each group/action:\")\n",
     "prob_rewards"
    ]
   },
@@ -203,12 +201,9 @@
    ],
    "source": [
     "# init simulation\n",
-    "sim = SimulationCmab(cmab=cmab, X=X, \n",
-    "                     group=group, \n",
-    "                     batch_size=batch_size, \n",
-    "                     n_updates=n_updates,\n",
-    "                     prob_rewards=prob_rewards, \n",
-    "                     verbose=verbose)"
+    "sim = SimulationCmab(\n",
+    "    cmab=cmab, X=X, group=group, batch_size=batch_size, n_updates=n_updates, prob_rewards=prob_rewards, verbose=verbose\n",
+    ")"
    ]
   },
   {

diff --git a/docs/src/tutorials/simulation_smab.ipynb b/docs/src/tutorials/simulation_smab.ipynb
@@ -41,7 +41,7 @@
    "outputs": [],
    "source": [
     "# define actions\n",
-    "action_ids = ['Action A', 'Action B', 'Action C']"
+    "action_ids = [\"Action A\", \"Action B\", \"Action C\"]"
    ]
   },
   {
@@ -70,11 +70,13 @@
    "outputs": [],
    "source": [
     "# init simulation\n",
-    "sim = SimulationSmab(smab=smab,\n",
-    "                     n_updates=20,\n",
-    "                     batch_size=2000,\n",
-    "                     probs_reward={'Action A': 0.6, 'Action B': 0.5, 'Action C': 0.8},\n",
-    "                     verbose=True)"
+    "sim = SimulationSmab(\n",
+    "    smab=smab,\n",
+    "    n_updates=20,\n",
+    "    batch_size=2000,\n",
+    "    probs_reward={\"Action A\": 0.6, \"Action B\": 0.5, \"Action C\": 0.8},\n",
+    "    verbose=True,\n",
+    ")"
    ]
   },
   {

diff --git a/docs/src/tutorials/smab.ipynb b/docs/src/tutorials/smab.ipynb
@@ -57,7 +57,7 @@
    "outputs": [],
    "source": [
     "# define actions\n",
-    "action_ids = ['Action A', 'Action B', 'Action C']"
+    "action_ids = [\"Action A\", \"Action B\", \"Action C\"]"
    ]
   },
   {
@@ -67,13 +67,9 @@
    "outputs": [],
    "source": [
     "# define beta priors parameters\n",
-    "success_priors = {'Action A': 1,\n",
-    "                  'Action B': 1,\n",
-    "                  'Action C': 1}\n",
+    "success_priors = {\"Action A\": 1, \"Action B\": 1, \"Action C\": 1}\n",
     "\n",
-    "failure_priors = {'Action A': 1,\n",
-    "                  'Action B': 1,\n",
-    "                  'Action C': 1}"
+    "failure_priors = {\"Action A\": 1, \"Action B\": 1, \"Action C\": 1}"
    ]
   },
   {
@@ -90,9 +86,7 @@
    "outputs": [],
    "source": [
     "# init stochastic Multi-Armed Bandit model\n",
-    "smab = Smab(action_ids=action_ids, \n",
-    "            success_priors=success_priors,\n",
-    "            failure_priors=failure_priors)"
+    "smab = Smab(action_ids=action_ids, success_priors=success_priors, failure_priors=failure_priors)"
    ]
   },
   {
@@ -118,7 +112,7 @@
    "source": [
     "# predict actions\n",
     "pred_actions, _ = smab.predict(n_samples=1000)\n",
-    "print('Recommended action: {}'.format(pred_actions[:10]))"
+    "print(\"Recommended action: {}\".format(pred_actions[:10]))"
    ]
   },
   {
@@ -149,7 +143,7 @@
     "for a in action_ids:\n",
     "    n_successes[a] = random.randint(0, pred_actions.count(a))\n",
     "    n_failures[a] = pred_actions.count(a) - n_successes[a]\n",
-    "    print('{}: n_successes={}, n_failures={}'.format(a, n_successes[a], n_failures[a]))"
+    "    print(\"{}: n_successes={}, n_failures={}\".format(a, n_successes[a], n_failures[a]))"
    ]
   },
   {

diff --git a/docs/tutorials/cmab.ipynb b/docs/tutorials/cmab.ipynb
@@ -86,7 +86,7 @@
     "n_samples = 1000\n",
     "n_features = 5\n",
     "X = 2 * np.random.random_sample((n_samples, n_features)) - 1  # random float in the interval (-1, 1)\n",
-    "print('X: context matrix of shape (n_samples, n_features)')\n",
+    "print(\"X: context matrix of shape (n_samples, n_features)\")\n",
     "print(X[:10])"
    ]
   },
@@ -97,7 +97,7 @@
    "outputs": [],
    "source": [
     "# define actions\n",
-    "actions_ids= ['action A', 'action B', 'action C']"
+    "actions_ids = [\"action A\", \"action B\", \"action C\"]"
    ]
   },
   {
@@ -140,8 +140,8 @@
    ],
    "source": [
     "# predict action\n",
-    "pred_actions, _ = cmab.predict(X) \n",
-    "print('Recommended action: {}'.format(pred_actions[:10]))"
+    "pred_actions, _ = cmab.predict(X)\n",
+    "print(\"Recommended action: {}\".format(pred_actions[:10]))"
    ]
   },
   {
@@ -167,7 +167,7 @@
    "source": [
     "# simulate reward from environment\n",
     "simulated_rewards = np.random.randint(2, size=n_samples)\n",
-    "print('Simulated rewards: {}'.format(simulated_rewards[:10]))"
+    "print(\"Simulated rewards: {}\".format(simulated_rewards[:10]))"
    ]
   },
   {