Skip to content

Commit

Permalink
Merge pull request #432 from rasbt/antecedent
Browse files Browse the repository at this point in the history 
Fix antecedEnt typo in docs
  • Loading branch information
@rasbt
rasbt committed Sep 7, 2018
2 parents 455f69d + d6f9fbf commit f3ac85e
Showing 1 changed file with 18 additions and 18 deletions.
36 changes: 18 additions & 18 deletions docs/sources/user_guide/frequent_patterns/association_rules.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@
"\n",
"### Metrics\n",
"\n",
"The currently supported metrics for evaluating association rules and setting selection thresholds are listed below. Given a rule \"A -> C\", *A* stands for antecedant and *C* stands for consequent.\n",
"The currently supported metrics for evaluating association rules and setting selection thresholds are listed below. Given a rule \"A -> C\", *A* stands for antecedent and *C* stands for consequent.\n",
"\n",
"\n",
"#### 'support':\n",
Expand All @@ -46,7 +46,7 @@
"\n",
"- introduced in [3]\n",
"\n",
"The support metric is defined for itemsets, not assocication rules. The table produced by the association rule mining algorithm contains three different support metrics: 'antecedant support', 'consequent support', and 'support'. Here, 'antecedant support' computes the proportion of transactions that contain the antecedant A, and 'consequent support' computes the support for the itemset of the consequent C. The 'support' metric then computes the support of the combined itemset A $\\cup$ C -- note that 'support' depends on 'antecedant support' and 'consequent support' via min('antecedant support', 'consequent support').\n",
"The support metric is defined for itemsets, not assocication rules. The table produced by the association rule mining algorithm contains three different support metrics: 'antecedent support', 'consequent support', and 'support'. Here, 'antecedent support' computes the proportion of transactions that contain the antecedent A, and 'consequent support' computes the support for the itemset of the consequent C. The 'support' metric then computes the support of the combined itemset A $\\cup$ C -- note that 'support' depends on 'antecedent support' and 'consequent support' via min('antecedent support', 'consequent support').\n",
"\n",
"\n",
"Typically, support is used to measure the abundance or frequency (often interpreted as significance or importance) of an itemset in a database. We refer to an itemset as a \"frequent itemset\" if you support is larger than a specified minimum-support threshold. Note that in general, due to the *downward closure* property, all subsets of a frequent itemset are also frequent.\n",
Expand Down Expand Up @@ -659,7 +659,7 @@
"source": [
"Pandas `DataFrames` make it easy to filter the results further. Let's say we are ony interested in rules that satisfy the following criteria:\n",
"\n",
"1. at least 2 antecedants\n",
"1. at least 2 antecedents\n",
"2. a confidence > 0.75\n",
"3. a lift score > 1.2\n",
"\n",
Expand Down Expand Up @@ -701,7 +701,7 @@
" <th>lift</th>\n",
" <th>leverage</th>\n",
" <th>conviction</th>\n",
" <th>antecedant_len</th>\n",
" <th>antecedent_len</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
Expand Down Expand Up @@ -804,7 +804,7 @@
"4 0.8 0.6 1.00 1.25 0.12 inf \n",
"5 0.6 0.6 0.75 1.25 0.12 1.600000 \n",
"\n",
" antecedant_len \n",
" antecedent_len \n",
"0 1 \n",
"1 1 \n",
"2 2 \n",
Expand All @@ -819,7 +819,7 @@
}
],
"source": [
"rules[\"antecedant_len\"] = rules[\"antecedents\"].apply(lambda x: len(x))\n",
"rules[\"antecedent_len\"] = rules[\"antecedents\"].apply(lambda x: len(x))\n",
"rules"
]
},
Expand Down Expand Up @@ -865,7 +865,7 @@
" <th>lift</th>\n",
" <th>leverage</th>\n",
" <th>conviction</th>\n",
" <th>antecedant_len</th>\n",
" <th>antecedent_len</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
Expand All @@ -890,7 +890,7 @@
" antecedents consequents antecedent support consequent support \\\n",
"2 (Onion, Kidney Beans) (Eggs) 0.6 0.8 \n",
"\n",
" support confidence lift leverage conviction antecedant_len \n",
" support confidence lift leverage conviction antecedent_len \n",
"2 0.6 1.0 1.25 0.12 inf 2 "
]
},
Expand All @@ -900,7 +900,7 @@
}
],
"source": [
"rules[ (rules['antecedant_len'] >= 2) &\n",
"rules[ (rules['antecedent_len'] >= 2) &\n",
" (rules['confidence'] > 0.75) &\n",
" (rules['lift'] > 1.2) ]"
]
Expand All @@ -909,7 +909,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, using the Pandas API, we can select entries based on the \"antecedants\" or \"consequents\" columns:"
"Similarly, using the Pandas API, we can select entries based on the \"antecedents\" or \"consequents\" columns:"
]
},
{
Expand Down Expand Up @@ -947,7 +947,7 @@
" <th>lift</th>\n",
" <th>leverage</th>\n",
" <th>conviction</th>\n",
" <th>antecedant_len</th>\n",
" <th>antecedent_len</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
Expand All @@ -972,7 +972,7 @@
" antecedents consequents antecedent support consequent support \\\n",
"3 (Kidney Beans, Eggs) (Onion) 0.8 0.6 \n",
"\n",
" support confidence lift leverage conviction antecedant_len \n",
" support confidence lift leverage conviction antecedent_len \n",
"3 0.6 0.75 1.25 0.12 1.6 2 "
]
},
Expand All @@ -993,13 +993,13 @@
"\n",
"Note that the entries in the \"itemsets\" column are of type `frozenset`, which is built-in Python type that is similar to a Python `set` but immutable, which makes it more efficient for certain query or comparison operations (https://docs.python.org/3.6/library/stdtypes.html#frozenset). Since `frozenset`s are sets, the item order does not matter. I.e., the query\n",
"\n",
"`rules[rules['antecedants'] == {'Eggs', 'Kidney Beans'}]`\n",
"`rules[rules['antecedents'] == {'Eggs', 'Kidney Beans'}]`\n",
" \n",
"is equivalent to any of the following three\n",
"\n",
"- `rules[rules['antecedants'] == {'Kidney Beans', 'Eggs'}]`\n",
"- `rules[rules['antecedants'] == frozenset(('Eggs', 'Kidney Beans'))]`\n",
"- `rules[rules['antecedants'] == frozenset(('Kidney Beans', 'Eggs'))]`\n",
"- `rules[rules['antecedents'] == {'Kidney Beans', 'Eggs'}]`\n",
"- `rules[rules['antecedents'] == frozenset(('Eggs', 'Kidney Beans'))]`\n",
"- `rules[rules['antecedents'] == frozenset(('Kidney Beans', 'Eggs'))]`\n",
"\n",
"\n"
]
Expand Down Expand Up @@ -1708,14 +1708,14 @@
"\n",
"**Returns**\n",
"\n",
"pandas DataFrame with columns \"antecedants\" and \"consequents\"\n",
"pandas DataFrame with columns \"antecedents\" and \"consequents\"\n",
" that store itemsets, plus the scoring metric columns:\n",
" \"antecedent support\", \"consequent support\",\n",
" \"support\", \"confidence\", \"lift\",\n",
" \"leverage\", \"conviction\"\n",
" of all rules for which\n",
" metric(rule) >= min_threshold.\n",
" Each entry in the \"antecedants\" and \"consequents\" columns are\n",
" Each entry in the \"antecedents\" and \"consequents\" columns are\n",
" of type `frozenset`, which is a Python built-in type that\n",
" behaves similarly to sets except that it is immutable\n",
" (For more info, see\n",
Expand Down

0 comments on commit f3ac85e

Please sign in to comment.